title
MIT 6.S091: Introduction to Deep Reinforcement Learning (Deep RL)

description
First lecture of MIT course 6.S091: Deep Reinforcement Learning, introducing the fascinating field of Deep RL. For more lecture videos on deep learning, reinforcement learning (RL), artificial intelligence (AI & AGI), and podcast conversations, visit our website or follow TensorFlow code tutorials on our GitHub repo. INFO: Website: https://deeplearning.mit.edu GitHub: https://github.com/lexfridman/mit-deep-learning Slides: http://bit.ly/2HtcoHV Playlist: http://bit.ly/deep-learning-playlist OUTLINE: 0:00 - Introduction 2:14 - Types of learning 6:35 - Reinforcement learning in humans 8:22 - What can be learned from data? 12:15 - Reinforcement learning framework 14:06 - Challenge for RL in real-world applications 15:40 - Component of an RL agent 17:42 - Example: robot in a room 23:05 - AI safety and unintended consequences 26:21 - Examples of RL systems 29:52 - Takeaways for real-world impact 31:25 - 3 types of RL: model-based, value-based, policy-based 35:28 - Q-learning 38:40 - Deep Q-Networks (DQN) 48:00 - Policy Gradient (PG) 50:36 - Advantage Actor-Critic (A2C & A3C) 52:52 - Deep Deterministic Policy Gradient (DDPG) 54:12 - Policy Optimization (TRPO and PPO) 56:03 - AlphaZero 1:00:50 - Deep RL in real-world applications 1:03:09 - Closing the RL simulation gap 1:04:44 - Next step in Deep RL CONNECT: - If you enjoyed this video, please subscribe to this channel. - Twitter: https://twitter.com/lexfridman - LinkedIn: https://www.linkedin.com/in/lexfridman - Facebook: https://www.facebook.com/lexfridman - Instagram: https://www.instagram.com/lexfridman

detail
{'title': 'MIT 6.S091: Introduction to Deep Reinforcement Learning (Deep RL)', 'heatmap': [{'end': 490.841, 'start': 445.725, 'weight': 1}], 'summary': 'Covers deep reinforcement learning, robot sensory data processing, challenges in aggregating state-of-the-art algorithms, rewarding and reinforcement learning, real-world applications, deep reinforcement learning and dqn, reinforcement learning advancements, model-based methods, and reinforcement learning in robotics and driving policy, emphasizing key techniques and real-world impact.', 'chapters': [{'end': 398.836, 'segs': [{'end': 137.52, 'src': 'embed', 'start': 107.438, 'weight': 0, 'content': [{'end': 113.401, 'text': "It's trial and error is the fundamental process by which reinforcement learning agents learn.", 'start': 107.438, 'duration': 5.963}, {'end': 119.425, 'text': 'And the deep part of deep reinforcement learning is neural networks.', 'start': 115.202, 'duration': 4.223}, {'end': 122.427, 'text': "It's using the frameworks of reinforcement,", 'start': 120.465, 'duration': 1.962}, {'end': 131.472, 'text': 'learning where the neural network is doing the representation of the world based on which the actions are made.', 'start': 122.427, 'duration': 9.045}, {'end': 137.52, 'text': 'And we have to take a step back when we look at the types of learning.', 'start': 133.899, 'duration': 3.621}], 'summary': 'Reinforcement learning agents use trial and error, leveraging neural networks for representation and action, emphasizing on types of learning.', 'duration': 30.082, 'max_score': 107.438, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/zR11FLZ-O9M/pics/zR11FLZ-O9M107438.jpg'}, {'end': 289.895, 'src': 'embed', 'start': 235.78, 'weight': 3, 'content': [{'end': 244.904, 'text': "But supervision nevertheless is required for any system that has an input and an output that's trying to learn, like a neural network does,", 'start': 235.78, 'duration': 9.124}, {'end': 246.565, 'text': "to provide an output that's good.", 'start': 244.904, 'duration': 1.661}, {'end': 249.386, 'text': "It needs somebody to say what's good and what's bad.", 'start': 247.085, 'duration': 2.301}, {'end': 251.587, 'text': 'For you curious about that.', 'start': 250.206, 'duration': 1.381}, {'end': 256.849, 'text': "there's been a few books, a couple written throughout the last few centuries, from Socrates to Nietzsche.", 'start': 251.587, 'duration': 5.262}, {'end': 259.089, 'text': 'I recommend the latter especially.', 'start': 256.849, 'duration': 2.24}, {'end': 263.672, 'text': "So let's look at supervised learning and reinforcement learning.", 'start': 260.231, 'duration': 3.441}, {'end': 273.392, 'text': "I'd like to propose a way to think about the difference that is illustrative and useful when we start talking about the techniques.", 'start': 263.692, 'duration': 9.7}, {'end': 289.895, 'text': "So supervised learning is taking a bunch of examples of data and learning from those examples where ground truth provides you the compressed semantic meaning of what's in that data.", 'start': 273.932, 'duration': 15.963}], 'summary': 'Supervision is crucial for learning systems like neural networks, especially in supervised learning using ground truth data.', 'duration': 54.115, 'max_score': 235.78, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/zR11FLZ-O9M/pics/zR11FLZ-O9M235780.jpg'}, {'end': 343.022, 'src': 'embed', 'start': 320.139, 'weight': 5, 'content': [{'end': 331.278, 'text': "then, for us now we'll talk about a bunch of algorithms, But the essential design step is to provide the world in which to experience.", 'start': 320.139, 'duration': 11.139}, {'end': 334.439, 'text': 'The agent learns from the world.', 'start': 332.198, 'duration': 2.241}, {'end': 340.281, 'text': 'From the world it gets the dynamics of that world, the physics of the world.', 'start': 336.26, 'duration': 4.021}, {'end': 343.022, 'text': "From that world it gets the rewards, what's good and bad.", 'start': 340.621, 'duration': 2.401}], 'summary': 'Algorithms learn from world dynamics to understand rewards.', 'duration': 22.883, 'max_score': 320.139, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/zR11FLZ-O9M/pics/zR11FLZ-O9M320139.jpg'}, {'end': 398.836, 'src': 'embed', 'start': 368.586, 'weight': 6, 'content': [{'end': 375.951, 'text': 'And the essential, perhaps the most difficult element of reinforcement learning is the reward, the good versus bad.', 'start': 368.586, 'duration': 7.365}, {'end': 388.053, 'text': 'Here a baby starts walking across the room We want to define success as a baby walking across the room and reaching the destination.', 'start': 377.031, 'duration': 11.022}, {'end': 388.893, 'text': "That's success.", 'start': 388.173, 'duration': 0.72}, {'end': 392.354, 'text': 'And failure is the inability to reach that destination.', 'start': 389.033, 'duration': 3.321}, {'end': 398.836, 'text': 'Simple And reinforcement learning in humans.', 'start': 392.874, 'duration': 5.962}], 'summary': "Reinforcement learning's key element is defining success and failure.", 'duration': 30.25, 'max_score': 368.586, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/zR11FLZ-O9M/pics/zR11FLZ-O9M368586.jpg'}], 'start': 0.089, 'title': 'Deep reinforcement learning overview, supervision in machine learning, and supervised vs reinforcement learning', 'summary': 'Introduces deep reinforcement learning and its use of deep neural networks for sequential decision-making, emphasizes the need for efficient supervision in reinforcement learning, and explains the difference between supervised learning and reinforcement learning, highlighting the role of examples, ground truth, experience, and rewards.', 'chapters': [{'end': 137.52, 'start': 0.089, 'title': 'Deep reinforcement learning overview', 'summary': 'Introduces deep reinforcement learning, explaining its significance in artificial intelligence and its use of deep neural networks for sequential decision-making, emphasizing trial and error learning. it highlights the marriage of deep neural networks and the ability to act on comprehension, as well as the fundamental process of reinforcement learning through trial and error.', 'duration': 137.431, 'highlights': ['The exciting field of deep reinforcement learning marries deep neural networks with the ability to act on comprehension. This highlights the significance of deep reinforcement learning in combining the power of deep neural networks with the ability to act on understanding.', 'Reinforcement learning agents learn through trial and error, emphasizing the fundamental process of learning. This emphasizes the fundamental process of learning in reinforcement learning, which is through trial and error.', 'Deep reinforcement learning uses neural networks to represent the world for making sequential decisions. This highlights the use of neural networks in deep reinforcement learning to represent the world for making sequential decisions.']}, {'end': 259.089, 'start': 138.121, 'title': 'Supervision in machine learning', 'summary': 'Explains that all types of machine learning involve some form of supervision, whether through manual annotation, loss functions, or human intervention, and emphasizes the need for efficient supervision in reinforcement learning.', 'duration': 120.968, 'highlights': ['The difference between supervised and unsupervised and reinforcement learning is the source of that supervision.', "Every type of machine learning is supervised learning. It's supervised by a loss function or a function that tells you what's good, and what's bad.", "At some point, there needs to be human intervention human input to provide what's good and what's bad.", 'The challenges and the exciting opportunities of reinforcement learning lie in the fact of how do we get that supervision in the most efficient way possible.', "Supervision nevertheless is required for any system that has an input and an output that's trying to learn, like a neural network does, to provide an output that's good."]}, {'end': 398.836, 'start': 260.231, 'title': 'Supervised vs reinforcement learning', 'summary': 'Explains the difference between supervised learning and reinforcement learning, emphasizing the role of examples and ground truth in supervised learning and the importance of experience and rewards in reinforcement learning.', 'duration': 138.605, 'highlights': ['Supervised learning involves learning from examples with ground truth providing compressed semantic meaning, while reinforcement learning teaches an agent through experience and rewards. Supervised learning relies on examples with ground truth to interpret future samples, while reinforcement learning teaches through experience and rewards.', 'The essential element of reinforcement learning is designing the world in which the agent experiences and learns, including understanding the dynamics, physics, and rewards of the world. Reinforcement learning requires designing the world for the agent to experience and learn, encompassing the dynamics, physics, and rewards of the world.', 'In reinforcement learning, defining success and failure through rewards is crucial, such as defining success as a baby walking across the room and reaching the destination, and failure as the inability to reach that destination. Reinforcement learning necessitates defining success and failure through rewards, such as defining success as reaching a destination and failure as the inability to do so.']}], 'duration': 398.747, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/zR11FLZ-O9M/pics/zR11FLZ-O9M89.jpg', 'highlights': ['Deep reinforcement learning combines deep neural networks with the ability to act on understanding.', 'Reinforcement learning emphasizes the fundamental process of learning through trial and error.', 'Deep reinforcement learning uses neural networks to represent the world for making sequential decisions.', 'Supervision is required for any system that has an input and an output trying to learn, like a neural network.', 'Reinforcement learning teaches through experience and rewards, unlike supervised learning which relies on examples with ground truth.', 'Reinforcement learning requires designing the world for the agent to experience and learn, encompassing the dynamics, physics, and rewards of the world.', 'Reinforcement learning necessitates defining success and failure through rewards, such as defining success as reaching a destination and failure as the inability to do so.']}, {'end': 660.192, 'segs': [{'end': 500.862, 'src': 'heatmap', 'start': 445.725, 'weight': 0, 'content': [{'end': 450.548, 'text': 'The ability to learn really quickly through observation, to aggregate that information,', 'start': 445.725, 'duration': 4.823}, {'end': 457.211, 'text': "filter all the junk that you don't need and be able to learn really quickly through imitation, learning through observation.", 'start': 450.548, 'duration': 6.663}, {'end': 461.293, 'text': 'For walking, that might mean observing others to walk.', 'start': 458.271, 'duration': 3.022}, {'end': 470.511, 'text': 'The idea there is if there was no others around, we would never be able to learn the fundamentals of this walking or as efficiently.', 'start': 462.247, 'duration': 8.264}, {'end': 472.392, 'text': "It's through observation.", 'start': 471.431, 'duration': 0.961}, {'end': 476.254, 'text': 'And then it could be the algorithm.', 'start': 474.553, 'duration': 1.701}, {'end': 481.676, 'text': 'Totally not understood is the algorithm that our brain uses to learn.', 'start': 476.474, 'duration': 5.202}, {'end': 490.841, 'text': "The back propagation that's in artificial neural networks, the same kind of processes not understood in the brain, that could be the key.", 'start': 482.137, 'duration': 8.704}, {'end': 499.361, 'text': 'So I want you to think about that, as we talk about the very trivial by comparison accomplishments and reinforcement learning,', 'start': 491.715, 'duration': 7.646}, {'end': 500.862, 'text': 'and how do we take the next steps?', 'start': 499.361, 'duration': 1.501}], 'summary': 'Learning through observation is key for quick and efficient learning, with unexplored brain algorithms.', 'duration': 75.842, 'max_score': 445.725, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/zR11FLZ-O9M/pics/zR11FLZ-O9M445725.jpg'}, {'end': 578.699, 'src': 'embed', 'start': 552.708, 'weight': 5, 'content': [{'end': 557.03, 'text': 'Us humans have several sensory systems.', 'start': 552.708, 'duration': 4.322}, {'end': 565.693, 'text': 'On cars, you can have LIDAR camera, stereo vision, audio, microphone, networking, GPS, IMU sensors, so on.', 'start': 557.61, 'duration': 8.083}, {'end': 569.215, 'text': "Whatever robot you can think about, there's a way to sense that world.", 'start': 566.174, 'duration': 3.041}, {'end': 572.056, 'text': 'and you have this raw sensory data.', 'start': 569.915, 'duration': 2.141}, {'end': 578.699, 'text': "and then, once you have the raw sensory data, you're tasked with representing that data in such a way that you can make sense of it,", 'start': 572.056, 'duration': 6.643}], 'summary': 'Humans and robots use various sensory systems for data representation and understanding.', 'duration': 25.991, 'max_score': 552.708, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/zR11FLZ-O9M/pics/zR11FLZ-O9M552708.jpg'}, {'end': 620.768, 'src': 'embed', 'start': 598.428, 'weight': 4, 'content': [{'end': 606.776, 'text': "That's exactly where deep learning neural networks have stepped in to be able to, in an automated fashion, with as little human input as possible,", 'start': 598.428, 'duration': 8.348}, {'end': 610.26, 'text': 'be able to form higher order representations of that information.', 'start': 606.776, 'duration': 3.484}, {'end': 620.768, 'text': "Then there's the learning aspect building on top of the greater abstractions formed through representations, be able to accomplish something useful,", 'start': 611.826, 'duration': 8.942}], 'summary': 'Deep learning neural networks automate forming higher order representations with minimal human input, enabling the accomplishment of useful tasks.', 'duration': 22.34, 'max_score': 598.428, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/zR11FLZ-O9M/pics/zR11FLZ-O9M598428.jpg'}, {'end': 660.192, 'src': 'embed', 'start': 638.055, 'weight': 7, 'content': [{'end': 653.21, 'text': "And then there is the ability to aggregate all the information that's been received in the past to the useful information that's pertinent to the task at hand.", 'start': 638.055, 'duration': 15.155}, {'end': 658.331, 'text': "It's the thing, the old, it looks like a duck, quacks like a duck, swims like a duck.", 'start': 653.91, 'duration': 4.421}, {'end': 660.192, 'text': 'Three different data sets.', 'start': 659.052, 'duration': 1.14}], 'summary': 'Ability to aggregate past information for task at hand, 3 data sets.', 'duration': 22.137, 'max_score': 638.055, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/zR11FLZ-O9M/pics/zR11FLZ-O9M638055.jpg'}], 'start': 398.836, 'title': 'Learning and robot sensory data processing', 'summary': 'Delves into the mystery of learning through trial and error, genetic encoding, and observation, while also exploring robot sensory data processing using deep learning neural networks to accomplish useful tasks.', 'chapters': [{'end': 528.594, 'start': 398.836, 'title': 'The mystery of learning', 'summary': 'Explores the mystery of learning through trial and error, genetic encoding, observation, and the algorithm used by the brain, reflecting on the efficiency of comprehension and the potential influence of observation in learning, as it poses open questions about the learning process and reinforces the excitement of having machines that can learn.', 'duration': 129.758, 'highlights': ['The ability to learn through observation, filtering information efficiently, and imitating others, suggests a quick learning process which may occur in the first few minutes, hours, days, or months after birth.', 'The influence of observation in learning, such as observing others to walk, is highlighted as a fundamental aspect that contributes to efficient learning.', 'The chapter emphasizes the mystery surrounding the algorithm used by the brain to learn, drawing parallels to the back propagation in artificial neural networks, which remains not fully understood.', 'The immense amount of data, such as 230 million years of bipedal data and 500 million years of the ability to see, is mentioned, indicating the potential influence of historical genetic encoding on learning.']}, {'end': 660.192, 'start': 528.594, 'title': 'Robot sensory data processing', 'summary': 'Discusses the process of sensing, representing, learning, and aggregating sensory data for robots, utilizing various sensors and deep learning neural networks to form higher abstractions and accomplish useful tasks.', 'duration': 131.598, 'highlights': ['Deep learning neural networks automate the formation of higher order representations of sensory data with minimal human input.', 'Robots utilize various sensors such as LIDAR, camera, stereo vision, audio, microphone, networking, GPS, and IMU sensors to sense their environment.', 'The process involves representing raw sensory data into higher abstractions to make sense of it, similar to how humans reason from edges to corners to faces.', 'Learning involves building on the greater abstractions formed through representations to accomplish useful tasks, such as discriminative and generative tasks.', 'Aggregating past information to extract pertinent data for the task at hand is crucial for robot functionality.']}], 'duration': 261.356, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/zR11FLZ-O9M/pics/zR11FLZ-O9M398836.jpg', 'highlights': ['The ability to learn through observation and imitation suggests a quick learning process after birth.', 'Observation in learning, such as observing others to walk, is highlighted as a fundamental aspect.', 'The mystery surrounding the algorithm used by the brain to learn is emphasized, drawing parallels to artificial neural networks.', 'The immense amount of historical genetic encoding is mentioned, indicating its potential influence on learning.', 'Deep learning neural networks automate the formation of higher order representations of sensory data.', 'Robots utilize various sensors to sense their environment, involving representing raw sensory data into higher abstractions.', 'Learning involves building on greater abstractions formed through representations to accomplish useful tasks.', 'Aggregating past information to extract pertinent data for the task at hand is crucial for robot functionality.']}, {'end': 1007.432, 'segs': [{'end': 703.545, 'src': 'embed', 'start': 660.572, 'weight': 0, 'content': [{'end': 670.055, 'text': "I'm sure there's state of the art algorithms for the three image classification, audio recognition, video classification, activity recognition, so on.", 'start': 660.572, 'duration': 9.483}, {'end': 674.439, 'text': 'Aggregating those three together, is still an open problem.', 'start': 670.435, 'duration': 4.004}, {'end': 676.22, 'text': 'And that could be the last piece.', 'start': 674.779, 'duration': 1.441}, {'end': 679.342, 'text': 'Again, I want you to think about it as we think about reinforcement learning agents.', 'start': 676.3, 'duration': 3.042}, {'end': 681.043, 'text': 'How do we play?', 'start': 679.702, 'duration': 1.341}, {'end': 687.166, 'text': 'how do we transfer from the game of Atari to the game of Go, to the game of Dota,', 'start': 681.043, 'duration': 6.123}, {'end': 692.789, 'text': 'to the game of a robot navigating an uncertain environment in the real world?', 'start': 687.166, 'duration': 5.623}, {'end': 703.545, 'text': 'And once you have that, once you sense the raw world, once you have a representation of that world, then we need to act,', 'start': 695.031, 'duration': 8.514}], 'summary': 'State-of-the-art algorithms needed for image, audio, and video recognition. aggregating them remains an open problem.', 'duration': 42.973, 'max_score': 660.572, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/zR11FLZ-O9M/pics/zR11FLZ-O9M660572.jpg'}, {'end': 746.547, 'src': 'embed', 'start': 722.314, 'weight': 4, 'content': [{'end': 731.219, 'text': 'learning is going beyond and building an agent that uses that representation and acts to achieve success in the world.', 'start': 722.314, 'duration': 8.905}, {'end': 733.24, 'text': "That's super exciting.", 'start': 732.059, 'duration': 1.181}, {'end': 746.547, 'text': "The framework and the formulation of reinforcement learning at its simplest is that there's an environment and there's an agent that acts in that environment.", 'start': 734.841, 'duration': 11.706}], 'summary': 'Building an agent using reinforcement learning to achieve success in the world.', 'duration': 24.233, 'max_score': 722.314, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/zR11FLZ-O9M/pics/zR11FLZ-O9M722314.jpg'}, {'end': 930.273, 'src': 'embed', 'start': 897.716, 'weight': 2, 'content': [{'end': 907.919, 'text': 'One is to improve the algorithms, improve the ability of the algorithms to form policies that are transferable across all kinds of domains,', 'start': 897.716, 'duration': 10.203}, {'end': 910.98, 'text': 'including the real world, including especially the real world.', 'start': 907.919, 'duration': 3.061}, {'end': 914.581, 'text': 'So train in simulation, transfer to the real world.', 'start': 911.34, 'duration': 3.241}, {'end': 930.273, 'text': 'Or as we improve the simulation in such a way that the fidelity of the simulation increases to the point where the gap between reality and simulation is minimal,', 'start': 917.31, 'duration': 12.963}], 'summary': 'Improve algorithms to form transferable policies for real-world application.', 'duration': 32.557, 'max_score': 897.716, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/zR11FLZ-O9M/pics/zR11FLZ-O9M897716.jpg'}, {'end': 1017.315, 'src': 'embed', 'start': 982.135, 'weight': 3, 'content': [{'end': 985.656, 'text': 'how good is that ability to evaluate that?', 'start': 982.135, 'duration': 3.521}, {'end': 991.097, 'text': 'and then the model different from the environment from the perspective the agent.', 'start': 985.656, 'duration': 5.441}, {'end': 999.458, 'text': 'so the environment has a model based on which it operates and then the agent has a representation best understanding of that model.', 'start': 991.097, 'duration': 8.361}, {'end': 1007.432, 'text': 'so the purpose for an RL agent in this simply formulated framework is to maximize reward.', 'start': 999.458, 'duration': 7.974}, {'end': 1017.315, 'text': 'The way that the reward mathematically and practically is talked about is with a discounted framework.', 'start': 1008.494, 'duration': 8.821}], 'summary': 'Rl agent aims to maximize reward using discounted framework.', 'duration': 35.18, 'max_score': 982.135, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/zR11FLZ-O9M/pics/zR11FLZ-O9M982135.jpg'}], 'start': 660.572, 'title': 'Challenges in aggregating state-of-the-art algorithms and deep reinforcement learning overview', 'summary': 'Discusses challenges in aggregating state-of-the-art algorithms for image, audio, and video classification, and provides an overview of deep reinforcement learning, including its framework, components of an rl agent, and the challenges in real-world applications.', 'chapters': [{'end': 703.545, 'start': 660.572, 'title': 'Challenges in aggregating state-of-the-art algorithms', 'summary': 'Discusses the challenges in aggregating state-of-the-art algorithms for image, audio, and video classification, emphasizing the open problem of combining them and the need for reinforcement learning agents to adapt to different environments.', 'duration': 42.973, 'highlights': ['The challenge of aggregating state-of-the-art algorithms for image, audio, and video classification is still an open problem, requiring consideration in the context of reinforcement learning agents.', 'The need to transfer reinforcement learning agents from different games, such as Atari, Go, Dota, to real-world environments, highlights the complexity of adapting to uncertain, real-world scenarios.']}, {'end': 1007.432, 'start': 703.545, 'title': 'Deep reinforcement learning overview', 'summary': 'Discusses the framework and challenges of deep reinforcement learning, including the formulation, components of an rl agent, and the challenges in real-world applications, emphasizing the need for improved algorithms and simulations for successful transfer to the real world.', 'duration': 303.887, 'highlights': ['The challenge for RL in real-world applications is the need for improved algorithms and simulations to enable successful transfer to the real world, either through training in simulation and transferring to the real world or by improving simulation fidelity for direct transfer to the real world. The chapter emphasizes the challenge of RL in real-world applications, highlighting the need for improved algorithms and simulations for successful transfer to the real world, either through training in simulation and transferring to the real world or by improving simulation fidelity for direct transfer to the real world.', "The components of an RL agent include the policy, value function, and model, with the agent's purpose being to maximize reward in a simply formulated framework. The chapter outlines the components of an RL agent, including the policy, value function, and model, emphasizing the agent's purpose of maximizing reward in a simply formulated framework.", 'The framework of reinforcement learning involves an environment and an agent, where the agent senses the environment, takes action, and receives a reward, posing open questions about its applicability to various scenarios, such as human life and games like Go. The chapter explains the framework of reinforcement learning, highlighting the interaction between the environment and the agent, and raising open questions about its applicability to various scenarios, such as human life and games like Go.']}], 'duration': 346.86, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/zR11FLZ-O9M/pics/zR11FLZ-O9M660572.jpg', 'highlights': ['The challenge of aggregating state-of-the-art algorithms for image, audio, and video classification is still an open problem, requiring consideration in the context of reinforcement learning agents.', 'The need to transfer reinforcement learning agents from different games, such as Atari, Go, Dota, to real-world environments, highlights the complexity of adapting to uncertain, real-world scenarios.', 'The challenge for RL in real-world applications is the need for improved algorithms and simulations to enable successful transfer to the real world, either through training in simulation and transferring to the real world or by improving simulation fidelity for direct transfer to the real world.', "The components of an RL agent include the policy, value function, and model, with the agent's purpose being to maximize reward in a simply formulated framework.", 'The framework of reinforcement learning involves an environment and an agent, where the agent senses the environment, takes action, and receives a reward, posing open questions about its applicability to various scenarios, such as human life and games like Go.']}, {'end': 1474.797, 'segs': [{'end': 1049.022, 'src': 'embed', 'start': 1008.494, 'weight': 2, 'content': [{'end': 1017.315, 'text': 'The way that the reward mathematically and practically is talked about is with a discounted framework.', 'start': 1008.494, 'duration': 8.821}, {'end': 1020.816, 'text': 'So we discount further and further future reward.', 'start': 1017.775, 'duration': 3.041}, {'end': 1029.058, 'text': "So the reward that's farther into the future means less to us in terms of maximization than reward that's in the near term.", 'start': 1021.096, 'duration': 7.962}, {'end': 1037.28, 'text': 'And so why do we discount it? So first, a lot of it is a math trick to be able to prove certain aspects, analyze certain aspects of convergence.', 'start': 1029.637, 'duration': 7.643}, {'end': 1049.022, 'text': "And in general, on a more philosophical sense, because environments either are or can be thought of as stochastic, random, it's very difficult to.", 'start': 1038.532, 'duration': 10.49}], 'summary': 'Reward is discounted for future maximization, aiding convergence analysis in stochastic environments.', 'duration': 40.528, 'max_score': 1008.494, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/zR11FLZ-O9M/pics/zR11FLZ-O9M1008494.jpg'}, {'end': 1320.876, 'src': 'embed', 'start': 1299.602, 'weight': 0, 'content': [{'end': 1309.966, 'text': 'Two things the environment model, the dynamics just there in the trivial example, the stochastic nature, the difference between 80% and 100% and 50%.', 'start': 1299.602, 'duration': 10.364}, {'end': 1315.769, 'text': 'The model of the world, the environment has a big impact on what the optimal policy is.', 'start': 1309.966, 'duration': 5.803}, {'end': 1319.615, 'text': 'And the reward structure.', 'start': 1317.393, 'duration': 2.222}, {'end': 1320.876, 'text': 'most importantly,', 'start': 1319.615, 'duration': 1.261}], 'summary': 'Environment model impacts optimal policy and reward structure.', 'duration': 21.274, 'max_score': 1299.602, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/zR11FLZ-O9M/pics/zR11FLZ-O9M1299602.jpg'}, {'end': 1397.622, 'src': 'embed', 'start': 1368.456, 'weight': 1, 'content': [{'end': 1378.211, 'text': 'so you design the world, the parameters of that world, and you also design the reward structure, and it can have Transformative results.', 'start': 1368.456, 'duration': 9.755}, {'end': 1384.235, 'text': "slight variations in those parameters can be huge results, huge differences on the policy that's arrived.", 'start': 1378.211, 'duration': 6.024}, {'end': 1397.622, 'text': "Of course, the example I've shown before, I really love, is the impact of the changing reward structure might have unintended consequences.", 'start': 1384.715, 'duration': 12.907}], 'summary': 'Designing world parameters and reward structure can have transformative impacts and unintended consequences.', 'duration': 29.166, 'max_score': 1368.456, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/zR11FLZ-O9M/pics/zR11FLZ-O9M1368456.jpg'}], 'start': 1008.494, 'title': 'Rewarding and reinforcement learning', 'summary': 'Explores a discounted reward framework for rewarding and the impact of uncertainty, stochastic nature, and reward structure on determining the optimal policy in reinforcement learning, with transformative results and unintended consequences of variations in these parameters.', 'chapters': [{'end': 1049.022, 'start': 1008.494, 'title': 'Discounted reward framework', 'summary': 'Discusses the use of a discounted framework for rewarding, where future rewards are valued less than near-term rewards due to mathematical analysis and stochastic environments.', 'duration': 40.528, 'highlights': ['The reward is talked about using a discounted framework, valuing near-term rewards more than those farther into the future.', 'Discounting future rewards is a mathematical trick to prove and analyze certain aspects of convergence.', 'The use of a discounted framework is due to the stochastic nature of environments, making it difficult to predict future outcomes.']}, {'end': 1474.797, 'start': 1049.022, 'title': 'Robot in the room: reinforcement learning', 'summary': 'Explains the impact of uncertainty, stochastic nature, and reward structure on determining the optimal policy in reinforcement learning, highlighting the transformative results and unintended consequences of variations in these parameters.', 'duration': 425.775, 'highlights': ['The impact of the changing reward structure might have unintended consequences. Variations in reward structure can lead to unintended consequences with potentially detrimental costs in real-world systems.', 'The environment model and reward structure have a big impact on the optimal policy in reinforcement learning. The environment model and reward structure significantly influence the optimal policy in reinforcement learning, with slight variations in parameters leading to huge differences in results.', 'The transformative results of slight variations in environment parameters and reward structure. Slight variations in environment parameters and reward structure can lead to transformative results in reinforcement learning.']}], 'duration': 466.303, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/zR11FLZ-O9M/pics/zR11FLZ-O9M1008494.jpg', 'highlights': ['The environment model and reward structure significantly influence the optimal policy in reinforcement learning, with slight variations in parameters leading to huge differences in results.', 'Slight variations in environment parameters and reward structure can lead to transformative results in reinforcement learning.', 'The use of a discounted framework is due to the stochastic nature of environments, making it difficult to predict future outcomes.', 'The impact of the changing reward structure might have unintended consequences. Variations in reward structure can lead to unintended consequences with potentially detrimental costs in real-world systems.', 'Discounting future rewards is a mathematical trick to prove and analyze certain aspects of convergence.', 'The reward is talked about using a discounted framework, valuing near-term rewards more than those farther into the future.']}, {'end': 2381.192, 'segs': [{'end': 1583.924, 'src': 'embed', 'start': 1514.391, 'weight': 0, 'content': [{'end': 1524.055, 'text': 'we have to then encode that ability to take subtle risk into AI-based control algorithms, perception.', 'start': 1514.391, 'duration': 9.664}, {'end': 1529.638, 'text': "Then you have to think about, at the end of the day, there's an objective function.", 'start': 1524.836, 'duration': 4.802}, {'end': 1538.562, 'text': 'And if that objective function does not anticipate the green turbos that are to be collected and then result in some unintended consequences,', 'start': 1530.298, 'duration': 8.264}, {'end': 1547.358, 'text': 'it could have very negative effects, especially in situations that involve human life.', 'start': 1538.562, 'duration': 8.796}, {'end': 1549.96, 'text': "That's the field of AI safety,", 'start': 1548.339, 'duration': 1.621}, {'end': 1560.267, 'text': 'and some of the folks who talk about deep mind and open AI that are doing incredible work in RL also have groups that are working on AI safety for a very good reason.', 'start': 1549.96, 'duration': 10.307}, {'end': 1570.976, 'text': 'This is a problem that I believe that artificial intelligence will define some of the most impactful positive things in the 21st century.', 'start': 1561.068, 'duration': 9.908}, {'end': 1580.442, 'text': 'but I also believe we are nowhere close to solving some of the fundamental problems of AI safety that we also need to address as we develop those algorithms.', 'start': 1570.976, 'duration': 9.466}, {'end': 1583.924, 'text': 'Okay, examples of reinforcement learning systems.', 'start': 1581.423, 'duration': 2.501}], 'summary': 'Ai safety is crucial as it could have very negative effects, especially in situations involving human life. there are fundamental problems of ai safety that need to be addressed.', 'duration': 69.533, 'max_score': 1514.391, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/zR11FLZ-O9M/pics/zR11FLZ-O9M1514391.jpg'}, {'end': 1710.738, 'src': 'embed', 'start': 1685.471, 'weight': 5, 'content': [{'end': 1693.473, 'text': "And on the robotics platform, the object manipulation and grasping objects, there's a few benchmarks, there's a few interesting applications.", 'start': 1685.471, 'duration': 8.002}, {'end': 1702.095, 'text': 'Learning, the problem of grabbing objects, moving objects, manipulating objects, rotating and so on,', 'start': 1694.173, 'duration': 7.922}, {'end': 1705.656, 'text': "especially when those objects don't have complicated shapes.", 'start': 1702.095, 'duration': 3.561}, {'end': 1710.738, 'text': 'And so the goal is to pick up an object in the purely in the grasping object challenge.', 'start': 1706.755, 'duration': 3.983}], 'summary': 'Robotics platform aims to grasp and manipulate objects efficiently, especially those with simple shapes.', 'duration': 25.267, 'max_score': 1685.471, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/zR11FLZ-O9M/pics/zR11FLZ-O9M1685471.jpg'}, {'end': 1878.942, 'src': 'embed', 'start': 1834.104, 'weight': 3, 'content': [{'end': 1834.844, 'text': "That's the fun part.", 'start': 1834.104, 'duration': 0.74}, {'end': 1841.966, 'text': "The hard part is asking good questions and collecting huge amounts of data that's representative of the task.", 'start': 1836.344, 'duration': 5.622}, {'end': 1845.387, 'text': "That's for real-world impact, not CVPR publication.", 'start': 1842.386, 'duration': 3.001}, {'end': 1848.247, 'text': 'Real-world impact, a huge amount of data.', 'start': 1845.527, 'duration': 2.72}, {'end': 1854.727, 'text': 'On a deeper enforcement learning side, the key challenge The fun part again is the algorithms.', 'start': 1848.867, 'duration': 5.86}, {'end': 1857.529, 'text': "How do we learn from data? Some of the stuff I'll talk about today.", 'start': 1855.007, 'duration': 2.522}, {'end': 1863.673, 'text': 'The hard part is defining the environment, defining the access space and the reward structure.', 'start': 1858.509, 'duration': 5.164}, {'end': 1866.234, 'text': 'As I mentioned, this is the big challenge.', 'start': 1864.373, 'duration': 1.861}, {'end': 1870.717, 'text': 'And the hardest part is how to crack the gap between simulation and the real world.', 'start': 1866.634, 'duration': 4.083}, {'end': 1872.018, 'text': 'The leaping lizard.', 'start': 1871.137, 'duration': 0.881}, {'end': 1873.999, 'text': "That's the hardest part.", 'start': 1873.118, 'duration': 0.881}, {'end': 1878.942, 'text': "We don't even know how to solve that transfer learning problem yet for the real world impact.", 'start': 1874.259, 'duration': 4.683}], 'summary': 'The challenge lies in collecting representative data for real-world impact and bridging the gap between simulation and the real world.', 'duration': 44.838, 'max_score': 1834.104, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/zR11FLZ-O9M/pics/zR11FLZ-O9M1834104.jpg'}, {'end': 1999.445, 'src': 'embed', 'start': 1952.395, 'weight': 6, 'content': [{'end': 1958.998, 'text': "And so model-based methods, because they're constructing a model, if they can, are extremely sample efficient.", 'start': 1952.395, 'duration': 6.603}, {'end': 1966.93, 'text': "Because once you have a model, you can do all kinds of reasoning that doesn't require experiencing every possibility of that model.", 'start': 1960.128, 'duration': 6.802}, {'end': 1973.592, 'text': 'You can unroll the model to see how the world changes based on your actions.', 'start': 1966.97, 'duration': 6.622}, {'end': 1984.296, 'text': 'Value-based methods are ones that look to estimate the quality of states, the quality of taking a certain action in a certain state.', 'start': 1975.493, 'duration': 8.803}, {'end': 1991.44, 'text': "They're called off policy versus the last category that's on policy.", 'start': 1986.417, 'duration': 5.023}, {'end': 1993.181, 'text': 'What does it mean to be off policy?', 'start': 1991.68, 'duration': 1.501}, {'end': 1999.445, 'text': 'It means that they constantly, value-based agents,', 'start': 1993.902, 'duration': 5.543}], 'summary': 'Model-based methods are extremely sample efficient, enabling reasoning without experiencing every possibility.', 'duration': 47.05, 'max_score': 1952.395, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/zR11FLZ-O9M/pics/zR11FLZ-O9M1952395.jpg'}, {'end': 2058.384, 'src': 'embed', 'start': 2027.27, 'weight': 8, 'content': [{'end': 2029.993, 'text': 'And then every once in a while flip a coin in order to explore.', 'start': 2027.27, 'duration': 2.723}, {'end': 2036.359, 'text': 'And then policy-based methods are ones that directly learn a policy function.', 'start': 2031.274, 'duration': 5.085}, {'end': 2049.271, 'text': 'So they take as input the world, representation of that world with neural networks, and as output, a action, where the action is stochastic.', 'start': 2037.019, 'duration': 12.252}, {'end': 2054.014, 'text': "So okay, that's the range of model-based, value-based, and policy-based.", 'start': 2050.051, 'duration': 3.963}, {'end': 2058.384, 'text': "Here's an image from OpenAI that I really like.", 'start': 2055.501, 'duration': 2.883}], 'summary': 'Exploring different methods for learning policy functions with neural networks.', 'duration': 31.114, 'max_score': 2027.27, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/zR11FLZ-O9M/pics/zR11FLZ-O9M2027270.jpg'}, {'end': 2168.364, 'src': 'embed', 'start': 2140.748, 'weight': 9, 'content': [{'end': 2143.45, 'text': "And let's take a step back and think about what Q learning is.", 'start': 2140.748, 'duration': 2.702}, {'end': 2149.355, 'text': 'Q learning looks at the state action value function, Q.', 'start': 2144.711, 'duration': 4.644}, {'end': 2155.042, 'text': 'that estimates based on a particular policy or based on an optimal policy?', 'start': 2150.421, 'duration': 4.621}, {'end': 2158.943, 'text': 'how good is it to take an action in this state?', 'start': 2155.042, 'duration': 3.901}, {'end': 2168.364, 'text': 'The estimated reward if I take an action in this state and continue operating under an optimal policy.', 'start': 2160.303, 'duration': 8.061}], 'summary': 'Q learning estimates state-action value function for optimal policy.', 'duration': 27.616, 'max_score': 2140.748, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/zR11FLZ-O9M/pics/zR11FLZ-O9M2140748.jpg'}, {'end': 2309.132, 'src': 'embed', 'start': 2275.91, 'weight': 10, 'content': [{'end': 2277.512, 'text': 'as your agent learns more and more and more.', 'start': 2275.91, 'duration': 1.602}, {'end': 2284.68, 'text': "So, in the beginning, you explore a lot with epsilon of one and epsilon of zero in the end, when you're just acting greedy,", 'start': 2278.113, 'duration': 6.567}, {'end': 2289.305, 'text': 'based on your understanding of the world as represented by the Q value function.', 'start': 2284.68, 'duration': 4.625}, {'end': 2294.038, 'text': 'For non-neural network approaches, this is simply a table.', 'start': 2290.535, 'duration': 3.503}, {'end': 2296.981, 'text': 'This Q function is a table.', 'start': 2295.219, 'duration': 1.762}, {'end': 2301.345, 'text': 'Like I said, on the Y state X actions.', 'start': 2297.301, 'duration': 4.044}, {'end': 2309.132, 'text': "And in each cell, you have a reward that's a discounted reward that you estimate to be received there.", 'start': 2301.945, 'duration': 7.187}], 'summary': 'Agent learns using epsilon-greedy and q-value table for non-neural network approach.', 'duration': 33.222, 'max_score': 2275.91, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/zR11FLZ-O9M/pics/zR11FLZ-O9M2275910.jpg'}, {'end': 2381.192, 'src': 'embed', 'start': 2351.247, 'weight': 11, 'content': [{'end': 2355.09, 'text': 'Grayscale, every pixel has 256 values.', 'start': 2351.247, 'duration': 3.843}, {'end': 2364.236, 'text': "That's 256 to the power of whatever 84 times 84 times 4 is.", 'start': 2355.91, 'duration': 8.326}, {'end': 2369.761, 'text': "Whatever it is, it's significantly larger than the number of atoms in the universe.", 'start': 2365.798, 'duration': 3.963}, {'end': 2375.69, 'text': 'So the size of this Q table, if we use the traditional approach, is intractable.', 'start': 2370.768, 'duration': 4.922}, {'end': 2381.192, 'text': 'Neural networks to the rescue.', 'start': 2380.092, 'duration': 1.1}], 'summary': 'Grayscale pixels have 256 values, leading to a q table size larger than atoms in the universe. neural networks provide a solution.', 'duration': 29.945, 'max_score': 2351.247, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/zR11FLZ-O9M/pics/zR11FLZ-O9M2351247.jpg'}], 'start': 1476.178, 'title': 'Real-world applications of reinforcement learning', 'summary': 'Covers unintended consequences of ai, real-world impact of reinforcement learning, types of reinforcement learning, and q-learning and policy optimization, emphasizing the need for ai safety measures and discussing applications in robotics, autonomous vehicles, and various reinforcement learning types and challenges.', 'chapters': [{'end': 1583.924, 'start': 1476.178, 'title': 'Unintended consequences of ai in the real world', 'summary': 'Discusses the potential negative impacts of ai in real-world scenarios, particularly in the context of robotics and autonomous vehicles, emphasizing the need for ai safety measures to address fundamental problems.', 'duration': 107.746, 'highlights': ['AI safety in the context of robotics and autonomous vehicles, highlighting the need to encode the ability to take subtle risks into AI-based control algorithms and the potential negative effects on human life (e.g., unintended consequences) - 3', 'The importance of addressing fundamental problems in AI safety as AI technology continues to advance and define impactful developments in the 21st century - 2', 'The discussion of unintended consequences and the detrimental effects of AI in the real world, emphasizing the need for AI safety measures to mitigate risks and potential negative impacts, particularly in situations involving human life - 1']}, {'end': 1878.942, 'start': 1585.185, 'title': 'Real-world impact of reinforcement learning', 'summary': 'Discusses the application of reinforcement learning in real-world scenarios, including examples such as cart pole balancing, game playing, object manipulation, and autonomous driving, highlighting the challenges and key takeaways of using reinforcement learning agents.', 'duration': 293.757, 'highlights': ['The challenges of reinforcement learning include defining the environment, access space, and reward structure, and bridging the gap between simulation and the real world.', 'Real-world impact requires collecting huge amounts of representative data for tasks, posing the challenge of asking good questions and obtaining relevant data.', 'Examples of reinforcement learning applications include cart pole balancing, game playing, object manipulation, and autonomous driving, each with specific goals, states, actions, and reward structures.']}, {'end': 2084.37, 'start': 1880.403, 'title': 'Types of reinforcement learning', 'summary': 'Introduces the three types of reinforcement learning: model-based, value-based, and policy-based, highlighting their differences in sample efficiency and approach to learning optimal actions.', 'duration': 203.967, 'highlights': ['Model-based algorithms are extremely sample efficient as they construct a model of the world, enabling planning far into the future without experiencing every possibility, making them effective for reasoning and predicting future actions. Model-based algorithms are sample efficient as they construct a model of the world, enabling planning into the future without experiencing every possibility.', 'Value-based methods estimate the quality of states and actions, allowing for off-policy learning where agents constantly update the goodness of taking actions in a state to pick the optimal action, without directly learning a policy. Value-based methods enable off-policy learning by constantly updating the goodness of taking actions in a state to pick the optimal action.', "Policy-based methods directly learn a policy function, taking the world's representation as input and outputting a stochastic action, offering a different approach to learning optimal actions. Policy-based methods directly learn a policy function, taking the world's representation as input and outputting a stochastic action."]}, {'end': 2381.192, 'start': 2084.37, 'title': 'Q-learning and policy optimization', 'summary': "Delves into the distinction between policy optimization and q-learning, highlighting q-learning's ability to estimate the value of actions in a state, the exploration aspect through epsilon-greedy strategy, and the impracticality of traditional q-table approach in real-world problems, leading to the adoption of neural networks.", 'duration': 296.822, 'highlights': ['Q-learning estimates the value of actions in a state, aiding in selecting actions to maximize rewards. Q-learning directly assesses the state action value function, Q, to determine the best action to take for maximizing rewards.', 'Exploration in Q-learning is facilitated through the epsilon-greedy strategy, gradually decreasing epsilon as the agent learns more. Epsilon-greedy strategy allows for exploration by occasionally choosing random actions, with epsilon gradually decreasing as the agent gains more knowledge.', 'Traditional Q-table approach becomes impractical in real-world problems due to the immense size of sensory input, leading to the adoption of neural networks. The size of the Q-table becomes intractable for real-world problems with large sensory input, such as in arcade games, prompting the adoption of neural networks to address this challenge.']}], 'duration': 905.014, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/zR11FLZ-O9M/pics/zR11FLZ-O9M1476178.jpg', 'highlights': ['AI safety in robotics and autonomous vehicles, encoding subtle risks into AI-based control algorithms - 3', 'Addressing fundamental problems in AI safety as technology advances - 2', 'Unintended consequences and detrimental effects of AI, emphasizing the need for safety measures - 1', 'Challenges of reinforcement learning: defining environment, access space, reward structure - 4', 'Real-world impact requires collecting huge amounts of representative data, posing challenges - 5', 'Reinforcement learning applications: cart pole balancing, game playing, object manipulation, autonomous driving - 6', 'Model-based algorithms: sample efficiency, effective for reasoning and predicting future actions - 7', 'Value-based methods: estimate quality of states and actions, enabling off-policy learning - 8', 'Policy-based methods: directly learn a policy function, offering a different approach to learning optimal actions - 9', 'Q-learning: estimates value of actions in a state, aiding in maximizing rewards - 10', 'Exploration in Q-learning facilitated through epsilon-greedy strategy, gradually decreasing epsilon - 11', 'Traditional Q-table approach becomes impractical in real-world problems, leading to adoption of neural networks - 12']}, {'end': 2703.951, 'segs': [{'end': 2470.227, 'src': 'embed', 'start': 2442.506, 'weight': 0, 'content': [{'end': 2446.267, 'text': 'So using neural networks, what neural networks are good at, which is function approximators.', 'start': 2442.506, 'duration': 3.761}, {'end': 2448.328, 'text': "And that's DQN.", 'start': 2447.008, 'duration': 1.32}, {'end': 2455.831, 'text': 'Deep Q network was used to have the initial incredible nice results on arcade games,', 'start': 2448.448, 'duration': 7.383}, {'end': 2463.554, 'text': 'where the input is the raw sensory pixels with a few convolutional layers, fully connected layers, and the output is a set of actions.', 'start': 2455.831, 'duration': 7.723}, {'end': 2470.227, 'text': 'probability of taking that action, and then you sample that, and you choose the best action.', 'start': 2466.723, 'duration': 3.504}], 'summary': 'Dqn, using neural networks, achieved impressive results on arcade games with raw sensory pixels.', 'duration': 27.721, 'max_score': 2442.506, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/zR11FLZ-O9M/pics/zR11FLZ-O9M2442506.jpg'}, {'end': 2681.861, 'src': 'embed', 'start': 2633.106, 'weight': 1, 'content': [{'end': 2640.408, 'text': 'And then the other trick, simple, is, like I said, that there is, so the loss function has two Qs.', 'start': 2633.106, 'duration': 7.302}, {'end': 2645.234, 'text': "So it's a dragon chasing its own tail.", 'start': 2641.932, 'duration': 3.302}, {'end': 2650.657, 'text': "It's easy for the loss function to become unstable, so the training does not converge.", 'start': 2645.814, 'duration': 4.843}, {'end': 2659.041, 'text': 'So the trick of fixing a target network is taking one of the cues and only updating it every x steps, every thousand steps, and so on.', 'start': 2651.357, 'duration': 7.684}, {'end': 2661.723, 'text': 'And taking the same kind of network is just fixing it.', 'start': 2659.422, 'duration': 2.301}, {'end': 2667.826, 'text': 'So for the target network that defines the loss function, just keeping it fixed and only updating it regularly.', 'start': 2662.343, 'duration': 5.483}, {'end': 2671.268, 'text': "So you're chasing a fixed target with a loss function.", 'start': 2668.166, 'duration': 3.102}, {'end': 2673.756, 'text': 'as opposed to a dynamic one.', 'start': 2671.775, 'duration': 1.981}, {'end': 2679.5, 'text': 'So you can solve a lot of the Atari games with minimal effort.', 'start': 2674.597, 'duration': 4.903}, {'end': 2681.861, 'text': 'come up with some creative solutions here.', 'start': 2679.5, 'duration': 2.361}], 'summary': 'Fixing a target network every x steps stabilizes training for atari games.', 'duration': 48.755, 'max_score': 2633.106, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/zR11FLZ-O9M/pics/zR11FLZ-O9M2633106.jpg'}], 'start': 2381.812, 'title': 'Deep reinforcement learning and dqn', 'summary': 'Discusses the integration of neural networks in reinforcement learning, particularly in the context of q-value approximation, with a focus on deep q network (dqn) and its key techniques such as experience replay and target network fixing.', 'chapters': [{'end': 2703.951, 'start': 2381.812, 'title': 'Deep reinforcement learning and dqn', 'summary': 'Discusses the integration of neural networks in reinforcement learning, particularly in the context of q-value approximation, with a focus on deep q network (dqn) and its key techniques such as experience replay and target network fixing.', 'duration': 322.139, 'highlights': ['Deep Q Network (DQN) leverages neural networks to approximate the Q function, achieving superhuman performance in arcade games with raw sensory pixels as input. DQN utilizes neural networks for Q function estimation, demonstrating superhuman performance in arcade games with raw sensory pixels as input.', 'Two key techniques in DQN are experience replay and target network fixing, which contribute significantly to the stability and convergence of the training process. DQN incorporates experience replay and target network fixing as crucial techniques for stabilizing the training process and ensuring convergence.', 'The loss function in DQN involves two Q functions, posing challenges for stability, which is addressed by fixing a target network and updating it at regular intervals. The presence of two Q functions in the loss function introduces stability challenges, mitigated by maintaining a fixed target network and updating it periodically.']}], 'duration': 322.139, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/zR11FLZ-O9M/pics/zR11FLZ-O9M2381812.jpg', 'highlights': ['DQN achieves superhuman performance in arcade games with raw sensory pixels as input.', 'Experience replay and target network fixing significantly contribute to the stability and convergence of the training process.', 'Maintaining a fixed target network and updating it periodically mitigates stability challenges introduced by two Q functions in the loss function.']}, {'end': 3344.359, 'segs': [{'end': 2772.983, 'src': 'embed', 'start': 2725.449, 'weight': 0, 'content': [{'end': 2731.714, 'text': 'And the same kind of DQN network is able to achieve superhuman performance on a bunch of different games.', 'start': 2725.449, 'duration': 6.265}, {'end': 2735.336, 'text': "There's improvements to this like dual DQN.", 'start': 2733.154, 'duration': 2.182}, {'end': 2744.082, 'text': "Again, the Q function can be decomposed, which is useful, into the value estimate of being in that state and what's called.", 'start': 2735.476, 'duration': 8.606}, {'end': 2747.104, 'text': 'in future, slides will be called advantage.', 'start': 2744.082, 'duration': 3.022}, {'end': 2749.866, 'text': 'So the advantage of taking action in that state.', 'start': 2747.684, 'duration': 2.182}, {'end': 2761.794, 'text': "The nice thing of the advantage as a measure is that it's a measure of the action quality relative to the average action that could be taken there.", 'start': 2750.246, 'duration': 11.548}, {'end': 2764.858, 'text': "So that's very useful.", 'start': 2762.316, 'duration': 2.542}, {'end': 2772.983, 'text': 'advantage versus sort of raw reward is that if all the actions you have to take are pretty good, you want to know well how much better it is.', 'start': 2764.858, 'duration': 8.125}], 'summary': 'Dqn network achieves superhuman performance on various games, with improvements like dual dqn and decomposed q function for action quality assessment.', 'duration': 47.534, 'max_score': 2725.449, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/zR11FLZ-O9M/pics/zR11FLZ-O9M2725449.jpg'}, {'end': 2827.606, 'src': 'embed', 'start': 2799.008, 'weight': 2, 'content': [{'end': 2808.192, 'text': 'for also when the there are many states in which the action is decoupled, the quality of the actions is decoupled from the state.', 'start': 2799.008, 'duration': 9.184}, {'end': 2818.139, 'text': "So many states, it doesn't matter which action you take, so you don't need to learn all the different complexities,", 'start': 2808.793, 'duration': 9.346}, {'end': 2821.922, 'text': "all the topology of different actions when you're in a particular state.", 'start': 2818.139, 'duration': 3.783}, {'end': 2827.606, 'text': 'And another one is prioritize experience replay.', 'start': 2823.583, 'duration': 4.023}], 'summary': 'Decoupling action from state reduces complexity, prioritize experience replay.', 'duration': 28.598, 'max_score': 2799.008, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/zR11FLZ-O9M/pics/zR11FLZ-O9M2799008.jpg'}, {'end': 2887.787, 'src': 'embed', 'start': 2853.232, 'weight': 3, 'content': [{'end': 2864.079, 'text': 'So prioritized experience replay assigns a priority, a value based on the magnitude of the temporal difference learned error.', 'start': 2853.232, 'duration': 10.847}, {'end': 2878.284, 'text': 'So the stuff you have learned the most from is given a higher priority and therefore you get to see through the experience replay process that particular experience more often.', 'start': 2864.839, 'duration': 13.445}, {'end': 2884.086, 'text': 'Okay, moving on to policy gradients.', 'start': 2882.185, 'duration': 1.901}, {'end': 2887.787, 'text': 'This is on policy versus Q-learning off policy.', 'start': 2884.646, 'duration': 3.141}], 'summary': 'Prioritized experience replay assigns priority based on temporal difference learned error, enabling more frequent revisiting of high-learning experiences. policy gradients compare on-policy and off-policy methods.', 'duration': 34.555, 'max_score': 2853.232, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/zR11FLZ-O9M/pics/zR11FLZ-O9M2853232.jpg'}, {'end': 2991.666, 'src': 'embed', 'start': 2965.413, 'weight': 4, 'content': [{'end': 2970.798, 'text': "It's the reason that policy gradient methods are more inefficient, but it's still very surprising that it works at all.", 'start': 2965.413, 'duration': 5.385}, {'end': 2976.577, 'text': 'So the pros versus DQN, the value-based methods,', 'start': 2972.375, 'duration': 4.202}, {'end': 2981.34, 'text': "is that if the world is so messy that you can't learn a Q function the nice thing about policy gradient,", 'start': 2976.577, 'duration': 4.763}, {'end': 2985.683, 'text': "because it's learning the policy directly that it will at least learn a pretty good policy.", 'start': 2981.34, 'duration': 4.343}, {'end': 2991.666, 'text': "Usually, in many cases faster convergence, it's able to deal with stochastic policies.", 'start': 2986.663, 'duration': 5.003}], 'summary': 'Policy gradient methods are inefficient but surprisingly effective, providing faster convergence and the ability to handle stochastic policies.', 'duration': 26.253, 'max_score': 2965.413, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/zR11FLZ-O9M/pics/zR11FLZ-O9M2965413.jpg'}, {'end': 3067.069, 'src': 'embed', 'start': 3036.308, 'weight': 5, 'content': [{'end': 3047.051, 'text': 'Advantage actor critic methods, A to C, combining the best of value-based methods and policy-based methods.', 'start': 3036.308, 'duration': 10.743}, {'end': 3052.072, 'text': 'So having an actor, two networks.', 'start': 3048.751, 'duration': 3.321}, {'end': 3056.914, 'text': "An actor, which is policy-based, and that's the one that takes the actions.", 'start': 3052.312, 'duration': 4.602}, {'end': 3063.519, 'text': 'samples the actions from the policy network and the critic that measures how good those actions are.', 'start': 3058.044, 'duration': 5.475}, {'end': 3065.624, 'text': 'And the critic is value-based.', 'start': 3064.261, 'duration': 1.363}, {'end': 3067.069, 'text': 'All right.', 'start': 3066.749, 'duration': 0.32}], 'summary': 'Advantage actor critic (a to c) method combines value-based and policy-based methods with an actor and a critic network.', 'duration': 30.761, 'max_score': 3036.308, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/zR11FLZ-O9M/pics/zR11FLZ-O9M3036308.jpg'}, {'end': 3233.306, 'src': 'embed', 'start': 3207.532, 'weight': 6, 'content': [{'end': 3215.595, 'text': "the problem, quite naturally, is that when the policy is now deterministic, it's able to do continuous action space, but because it's deterministic,", 'start': 3207.532, 'duration': 8.063}, {'end': 3216.615, 'text': "it's never exploring.", 'start': 3215.595, 'duration': 1.02}, {'end': 3221.557, 'text': 'So the way we inject exploration into the system is by adding noise,', 'start': 3217.076, 'duration': 4.481}, {'end': 3233.306, 'text': 'either adding noise into the action space on the output or adding noise into the parameters of the network that that create perturbations in the actions,', 'start': 3221.557, 'duration': 11.749}], 'summary': 'Policy needs exploration, so noise is added to action space or network parameters.', 'duration': 25.774, 'max_score': 3207.532, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/zR11FLZ-O9M/pics/zR11FLZ-O9M3207532.jpg'}, {'end': 3353.047, 'src': 'embed', 'start': 3319.724, 'weight': 7, 'content': [{'end': 3322.625, 'text': 'The problem with that is that can get you into trouble.', 'start': 3319.724, 'duration': 2.901}, {'end': 3326.467, 'text': 'Here is a nice visualization walking along a ridge.', 'start': 3322.725, 'duration': 3.742}, {'end': 3331.09, 'text': 'It can result in you stepping off that ridge.', 'start': 3328.388, 'duration': 2.702}, {'end': 3335.032, 'text': 'Again, the collapsing of the training process, the performance.', 'start': 3331.15, 'duration': 3.882}, {'end': 3344.359, 'text': 'The truss region is the underlying idea here for the for the policy optimization methods that first pick the step size,', 'start': 3335.472, 'duration': 8.887}, {'end': 3353.047, 'text': "so the constraint in various kinds of ways, the magnitude of the difference to the weights that's applied and then the direction.", 'start': 3344.359, 'duration': 8.688}], 'summary': 'Challenges arise from visualization, training process, and policy optimization methods.', 'duration': 33.323, 'max_score': 3319.724, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/zR11FLZ-O9M/pics/zR11FLZ-O9M3319724.jpg'}], 'start': 2703.951, 'title': 'Reinforcement learning advancements', 'summary': 'Explores the capabilities of neural networks surpassing human performance in games, including dqn networks achieving superhuman performance, advantage as a measure of action quality, dueling nature of ddqn, prioritized experience replay, policy gradient methods, advantage actor critic methods, a3c, ddpg, asynchronous learning, and careful policy optimization.', 'chapters': [{'end': 3012.657, 'start': 2703.951, 'title': 'Advancements in reinforcement learning', 'summary': 'Discusses the capabilities of neural networks in surpassing human creativity and performance in games, highlighting the use of dqn networks achieving superhuman performance, the concept of advantage as a measure of action quality, the dueling nature of ddqn, and the benefits of prioritized experience replay and policy gradient methods.', 'duration': 308.706, 'highlights': ['The DQN network is able to achieve superhuman performance on various games, demonstrating its capability to surpass human performance (quantifiable data: superhuman performance).', 'The advantage measure in value-based optimization provides a better measure for choosing actions, especially when all actions that need to be taken are of good quality (quantifiable data: measure of action quality relative to average action).', 'The concept of dueling DQN, where one stream estimates the value and the other estimates the advantage, is useful for decoupling the quality of actions from the state, particularly in states where the action quality is decoupled from the state (quantifiable data: decoupling the quality of actions from the state).', 'Prioritized experience replay assigns a priority based on the magnitude of the temporal difference learned error, allowing experiences that have been learned the most from to be given a higher priority in the replay process (quantifiable data: assigns priority based on the magnitude of the temporal difference learned error).', 'Policy gradient methods are capable of learning a pretty good policy in messy environments, enabling faster convergence, handling stochastic policies, and dealing with continuous actions, although they are less efficient and can become highly unstable during training (quantifiable data: capability to learn in messy environments, faster convergence, handling stochastic policies and continuous actions, inefficiency, and potential instability).']}, {'end': 3344.359, 'start': 3013.137, 'title': 'Advancements in reinforcement learning', 'summary': 'Discusses the advantage actor critic methods, a3c and ddpg, highlighting the combination of value-based and policy-based methods, asynchronous learning, deterministic policy with exploration, and the importance of careful policy optimization in reinforcement learning.', 'duration': 331.222, 'highlights': ['The Advantage Actor Critic methods combine the best of value-based and policy-based methods, allowing for more sample-efficient learning and are highly parallelizable. The A2C method combines an actor, which is policy-based, and a critic, which is value-based, resulting in improved sample efficiency and highly parallelizable learning.', 'The Deep Deterministic Policy Gradient method deals with continuous action spaces by using a deterministic policy with exploration through the addition of noise. The DDPG method addresses continuous action spaces by using a deterministic policy with added noise to enable exploration, which decreases as learning progresses.', 'The importance of careful policy optimization is highlighted, emphasizing the need to avoid taking really bad actions to prevent the collapse of training performance. The chapter stresses the significance of careful policy optimization to prevent the collapse of training performance, utilizing methods such as line search to avoid taking detrimental actions.']}], 'duration': 640.408, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/zR11FLZ-O9M/pics/zR11FLZ-O9M2703951.jpg', 'highlights': ['The DQN network achieves superhuman performance on various games.', 'The advantage measure in value-based optimization provides a better measure for choosing actions.', 'Dueling DQN decouples the quality of actions from the state.', 'Prioritized experience replay assigns priority based on the magnitude of the temporal difference learned error.', 'Policy gradient methods are capable of learning in messy environments and handling stochastic policies and continuous actions.', 'The Advantage Actor Critic methods combine the best of value-based and policy-based methods, allowing for more sample-efficient learning and are highly parallelizable.', 'The Deep Deterministic Policy Gradient method deals with continuous action spaces by using a deterministic policy with exploration through the addition of noise.', 'Careful policy optimization is crucial to prevent the collapse of training performance.']}, {'end': 3640.018, 'segs': [{'end': 3487.131, 'src': 'embed', 'start': 3432.701, 'weight': 1, 'content': [{'end': 3448.527, 'text': "It's to learn which boards, which game positions are most likely to result in a, are most useful to explore and result in a highly successful state.", 'start': 3432.701, 'duration': 15.826}, {'end': 3456.351, 'text': "So that choice of what's good to explore, what branch is good to go down is where we can have neural networks step in.", 'start': 3449.208, 'duration': 7.143}, {'end': 3459.616, 'text': 'With AlphaGo, it was pre-trained.', 'start': 3457.332, 'duration': 2.284}, {'end': 3464.566, 'text': 'The first success that beat the world champion was pre-trained on expert games.', 'start': 3459.897, 'duration': 4.669}, {'end': 3473.981, 'text': 'Then with AlphaGo Zero, It was no pre-training on expert systems, so no imitation learning.', 'start': 3465.268, 'duration': 8.713}, {'end': 3480.366, 'text': "it's just purely through self-play, through suggesting, through playing itself, new board positions.", 'start': 3473.981, 'duration': 6.385}, {'end': 3487.131, 'text': 'Many of these systems use Monte Carlo tree search and during this search, balancing exploitation, exploration.', 'start': 3480.726, 'duration': 6.405}], 'summary': 'Using neural networks, alphago achieved success through self-play and no pre-training on expert systems, employing monte carlo tree search for balancing exploitation and exploration.', 'duration': 54.43, 'max_score': 3432.701, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/zR11FLZ-O9M/pics/zR11FLZ-O9M3432701.jpg'}, {'end': 3538.98, 'src': 'embed', 'start': 3511.759, 'weight': 0, 'content': [{'end': 3515.722, 'text': 'So estimating just general quality and probability of leading to victory.', 'start': 3511.759, 'duration': 3.963}, {'end': 3523.689, 'text': 'Then the next step forward is alpha zero using the same similar architecture with MCTS.', 'start': 3516.302, 'duration': 7.387}, {'end': 3527.371, 'text': 'multi-color tree search, but applying it to different games.', 'start': 3524.369, 'duration': 3.002}, {'end': 3535.858, 'text': 'And applying it and competing against other engines, state-of-the-art engines, in Go, in Shogi, in chess.', 'start': 3528.352, 'duration': 7.506}, {'end': 3538.98, 'text': 'And outperforming them with very few steps.', 'start': 3536.819, 'duration': 2.161}], 'summary': 'Alpha zero outperforms state-of-the-art engines in go, shogi, and chess with very few steps.', 'duration': 27.221, 'max_score': 3511.759, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/zR11FLZ-O9M/pics/zR11FLZ-O9M3511759.jpg'}], 'start': 3344.359, 'title': 'Model-based methods in deep learning', 'summary': "Discusses the use of neural networks in model-based methods, particularly in games like go and chess, highlighting the success of alphazero and alphago in learning board positions and balancing exploitation and exploration during self-play. it also explores alpha zero's use of neural networks and mcts to outperform state-of-the-art engines in go, shogi, and chess by exploring fewer branches and efficiently estimating the quality of game positions.", 'chapters': [{'end': 3511.298, 'start': 3344.359, 'title': 'Model-based methods in deep learning', 'summary': 'Discusses the use of neural networks in model-based methods, particularly in games like go and chess, highlighting the success of alphazero and alphago in learning board positions and balancing exploitation and exploration during self-play.', 'duration': 166.939, 'highlights': ['AlphaGo Zero achieved success through self-play without pre-training on expert systems, showcasing the effectiveness of neural networks in learning board positions through exploration.', 'The task for a neural network in games like Go is to learn the quality of the board and to determine which game positions are most useful to explore, leading to highly successful states.', 'The chapter emphasizes the exponential growth of game trees in turn-based games like Go, highlighting the significance of neural networks in learning and estimating the quality of different board positions.', "The use of Monte Carlo tree search in systems like AlphaGo involves balancing exploitation and exploration, demonstrating the neural network's role in estimating the potential success of different board positions."]}, {'end': 3640.018, 'start': 3511.759, 'title': "Alpha zero's advantages in defeating state-of-the-art game engines", 'summary': "Discusses alpha zero's use of neural networks and mcts to outperform state-of-the-art engines in go, shogi, and chess by exploring fewer branches and efficiently estimating the quality of game positions.", 'duration': 128.259, 'highlights': ["Alpha Zero outperforms state-of-the-art engines in Go, Shogi, and chess by exploring fewer branches and efficiently estimating the quality of game positions. Alpha Zero's use of neural networks and MCTS allows it to outperform engines in various games, such as Go, Shogi, and chess, by exploring fewer branches and efficiently estimating the quality of game positions.", "Stockfish can defeat most humans at chess, but Alpha Zero's model-based approaches and neural network enable it to outperform Stockfish. Alpha Zero's model-based approaches and neural network enable it to outperform Stockfish, which can defeat most humans at chess.", "Alpha Zero's ability to efficiently estimate the quality of a board allows it to defeat engines that are far superior to humans. Alpha Zero's efficient estimation of the quality of a board allows it to defeat engines that are far superior to humans in various games."]}], 'duration': 295.659, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/zR11FLZ-O9M/pics/zR11FLZ-O9M3344359.jpg', 'highlights': ['Alpha Zero outperforms state-of-the-art engines in Go, Shogi, and chess by exploring fewer branches and efficiently estimating the quality of game positions.', 'AlphaGo Zero achieved success through self-play without pre-training on expert systems, showcasing the effectiveness of neural networks in learning board positions through exploration.', "The use of Monte Carlo tree search in systems like AlphaGo involves balancing exploitation and exploration, demonstrating the neural network's role in estimating the potential success of different board positions.", 'The task for a neural network in games like Go is to learn the quality of the board and to determine which game positions are most useful to explore, leading to highly successful states.']}, {'end': 4025.835, 'segs': [{'end': 3770.118, 'src': 'embed', 'start': 3722.714, 'weight': 0, 'content': [{'end': 3727.838, 'text': "So it's quite exciting through RL to be able to learn some of the control dynamics here.", 'start': 3722.714, 'duration': 5.124}, {'end': 3733.842, 'text': "that's able to teach this particular robot to be able to get up from arbitrary positions.", 'start': 3727.838, 'duration': 6.004}, {'end': 3743.109, 'text': "So it's less hard coding in order to be able to deal with unexpected initial conditions and unexpected perturbations.", 'start': 3734.422, 'duration': 8.687}, {'end': 3747.189, 'text': "So it's exciting there in terms of learning the control dynamics.", 'start': 3743.888, 'duration': 3.301}, {'end': 3755.312, 'text': 'And some of the driving policy, so making driving behavior decisions, changing lanes turning and so on,', 'start': 3747.769, 'duration': 7.543}, {'end': 3759.494, 'text': 'that if you were here last week heard from Waymo.', 'start': 3755.312, 'duration': 4.182}, {'end': 3764.176, 'text': "they're starting to use some RL in terms of the driving policy in order to especially predict the future.", 'start': 3759.494, 'duration': 4.682}, {'end': 3770.118, 'text': "They're trying to anticipate intent modeling, predict where the pedestrians, where the cars are going to be, based on the environment.", 'start': 3764.696, 'duration': 5.422}], 'summary': 'Rl enables learning control dynamics and driving behavior decisions for robots and autonomous vehicles.', 'duration': 47.404, 'max_score': 3722.714, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/zR11FLZ-O9M/pics/zR11FLZ-O9M3722714.jpg'}, {'end': 3832.532, 'src': 'embed', 'start': 3798.233, 'weight': 3, 'content': [{'end': 3802.555, 'text': 'Most of the work is done from the design of the environment and the design of the reward structure.', 'start': 3798.233, 'duration': 4.322}, {'end': 3805.516, 'text': 'And because most of that work now is in simulation,', 'start': 3802.995, 'duration': 2.521}, {'end': 3812.82, 'text': 'we need to either develop better algorithms for transfer learning or close the distance between simulation and the real world.', 'start': 3805.516, 'duration': 7.304}, {'end': 3818.083, 'text': 'And also we could think outside the box a little bit.', 'start': 3813.5, 'duration': 4.583}, {'end': 3822.605, 'text': 'I had the conversation with Peter Beale recently, one of the leading researchers in Deep RL.', 'start': 3818.103, 'duration': 4.502}, {'end': 3832.532, 'text': "It kinda, on the side, quickly mention, the idea is that we don't need to make simulation more realistic.", 'start': 3823.45, 'duration': 9.082}], 'summary': 'Focus on improving transfer learning algorithms and bridging the gap between simulation and the real world in deep rl.', 'duration': 34.299, 'max_score': 3798.233, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/zR11FLZ-O9M/pics/zR11FLZ-O9M3798233.jpg'}, {'end': 3872.99, 'src': 'embed', 'start': 3845.094, 'weight': 4, 'content': [{'end': 3853.056, 'text': 'the regularization aspect of having all those simulations will make it so that our reality is just another sample from those simulations.', 'start': 3845.094, 'duration': 7.962}, {'end': 3859.183, 'text': "And so maybe the solution isn't to create higher fidelity simulation or to create transfer learning algorithms.", 'start': 3854.141, 'duration': 5.042}, {'end': 3866.787, 'text': "Maybe it's to build a arbitrary number of simulations.", 'start': 3860.024, 'duration': 6.763}, {'end': 3872.99, 'text': 'So then that step towards creating a agent that works in the real world is a trivial one.', 'start': 3867.427, 'duration': 5.563}], 'summary': 'Regularization through simulations may make our reality just another sample, suggesting the need for an arbitrary number of simulations to create a trivial step towards real-world agent development.', 'duration': 27.896, 'max_score': 3845.094, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/zR11FLZ-O9M/pics/zR11FLZ-O9M3845094.jpg'}, {'end': 3931.614, 'src': 'embed', 'start': 3902.96, 'weight': 5, 'content': [{'end': 3906.882, 'text': "You know, if you're interested in getting into research in RL, what are the steps you need to take?", 'start': 3902.96, 'duration': 3.922}, {'end': 3915.227, 'text': 'From the background of developing the mathematical background, ProbStat and multivariate calculus to some of the basics,', 'start': 3907.362, 'duration': 7.865}, {'end': 3919.05, 'text': 'like I covered last week on deep learning, some of the basic ideas in RL.', 'start': 3915.227, 'duration': 3.823}, {'end': 3921.911, 'text': 'just terminology and so on, some basic concepts.', 'start': 3919.67, 'duration': 2.241}, {'end': 3928.473, 'text': 'Then picking a framework, TensorFlow or PyTorch, and learn by doing.', 'start': 3922.311, 'duration': 6.162}, {'end': 3931.614, 'text': 'Implement the algorithms I mentioned today.', 'start': 3929.573, 'duration': 2.041}], 'summary': 'To get into research in rl, first build mathematical foundation, then learn basics of rl and deep learning, pick a framework, and implement algorithms.', 'duration': 28.654, 'max_score': 3902.96, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/zR11FLZ-O9M/pics/zR11FLZ-O9M3902960.jpg'}], 'start': 3640.018, 'title': 'Reinforcement learning in robotics and driving policy', 'summary': 'Explores the role of reinforcement learning in robotics, emphasizing the shift towards learning control dynamics through rl. it also delves into the use of rl in driving policy to predict future driving behavior and the transition from simulation to real world, along with creating simulations for better transfer learning.', 'chapters': [{'end': 3747.189, 'start': 3640.018, 'title': 'The role of reinforcement learning in robotics', 'summary': 'Highlights that the majority of real-world applications in robotics do not involve reinforcement learning (rl), including autonomous vehicles and robotic manipulation, but there is a shift towards learning control dynamics through rl, as seen in recent developments.', 'duration': 107.171, 'highlights': ['The majority of real-world applications in robotics, such as autonomous vehicles, robotic manipulation, and humanoid robots, do not involve reinforcement learning (RL) for learning actions from data.', 'Recent developments show a shift towards learning control dynamics through RL, aiming for more efficient and robust movement in robotics.', 'The exciting advancement in robotics involves using RL to teach robots to deal with unexpected initial conditions and perturbations, reducing the reliance on hard coding for such scenarios.']}, {'end': 4025.835, 'start': 3747.769, 'title': 'Rl in driving policy and simulations', 'summary': 'Discusses the use of reinforcement learning in driving policy to predict future driving behavior, the transition from simulation to real world, and the approach of creating a large number of simulations for better transfer learning, along with steps for research in rl.', 'duration': 278.066, 'highlights': ['The use of reinforcement learning in driving policy to predict future driving behavior and anticipate intent modeling. Waymo is starting to use RL for driving policy to predict future driving behavior and anticipate intent modeling.', 'The transition from simulation to real world and the need for better transfer learning algorithms. The challenge is the gap from simulation to real world and the need for better transfer learning algorithms or closing the distance between simulation and the real world.', 'The approach of creating a large number of simulations for better transfer learning. The idea of creating an infinite number of simulations for better transfer learning and regularization aspect for making reality another sample from those simulations.', 'Steps for research in RL including developing mathematical background, picking a framework, implementing core RL algorithms, and iterating fast on simple benchmark environments. Steps for research in RL include developing mathematical background, picking a framework, implementing core RL algorithms, and iterating fast on simple benchmark environments.']}], 'duration': 385.817, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/zR11FLZ-O9M/pics/zR11FLZ-O9M3640018.jpg', 'highlights': ['Recent developments show a shift towards learning control dynamics through RL, aiming for more efficient and robust movement in robotics.', 'The exciting advancement in robotics involves using RL to teach robots to deal with unexpected initial conditions and perturbations, reducing the reliance on hard coding for such scenarios.', 'The use of reinforcement learning in driving policy to predict future driving behavior and anticipate intent modeling.', 'The transition from simulation to real world and the need for better transfer learning algorithms.', 'The idea of creating an infinite number of simulations for better transfer learning and regularization aspect for making reality another sample from those simulations.', 'Steps for research in RL include developing mathematical background, picking a framework, implementing core RL algorithms, and iterating fast on simple benchmark environments.']}], 'highlights': ['Deep reinforcement learning combines deep neural networks with the ability to act on understanding.', 'Reinforcement learning emphasizes the fundamental process of learning through trial and error.', 'Deep reinforcement learning uses neural networks to represent the world for making sequential decisions.', 'Supervision is required for any system that has an input and an output trying to learn, like a neural network.', 'Reinforcement learning teaches through experience and rewards, unlike supervised learning which relies on examples with ground truth.', 'Reinforcement learning requires designing the world for the agent to experience and learn, encompassing the dynamics, physics, and rewards of the world.', 'Reinforcement learning necessitates defining success and failure through rewards, such as defining success as reaching a destination and failure as the inability to do so.', 'The ability to learn through observation and imitation suggests a quick learning process after birth.', 'Observation in learning, such as observing others to walk, is highlighted as a fundamental aspect.', 'The mystery surrounding the algorithm used by the brain to learn is emphasized, drawing parallels to artificial neural networks.', 'The immense amount of historical genetic encoding is mentioned, indicating its potential influence on learning.', 'Deep learning neural networks automate the formation of higher order representations of sensory data.', 'Robots utilize various sensors to sense their environment, involving representing raw sensory data into higher abstractions.', 'Learning involves building on greater abstractions formed through representations to accomplish useful tasks.', 'Aggregating past information to extract pertinent data for the task at hand is crucial for robot functionality.', 'The challenge of aggregating state-of-the-art algorithms for image, audio, and video classification is still an open problem, requiring consideration in the context of reinforcement learning agents.', 'The need to transfer reinforcement learning agents from different games, such as Atari, Go, Dota, to real-world environments, highlights the complexity of adapting to uncertain, real-world scenarios.', 'The challenge for RL in real-world applications is the need for improved algorithms and simulations to enable successful transfer to the real world, either through training in simulation and transferring to the real world or by improving simulation fidelity for direct transfer to the real world.', "The components of an RL agent include the policy, value function, and model, with the agent's purpose being to maximize reward in a simply formulated framework.", 'The framework of reinforcement learning involves an environment and an agent, where the agent senses the environment, takes action, and receives a reward, posing open questions about its applicability to various scenarios, such as human life and games like Go.', 'The environment model and reward structure significantly influence the optimal policy in reinforcement learning, with slight variations in parameters leading to huge differences in results.', 'Slight variations in environment parameters and reward structure can lead to transformative results in reinforcement learning.', 'The use of a discounted framework is due to the stochastic nature of environments, making it difficult to predict future outcomes.', 'The impact of the changing reward structure might have unintended consequences. Variations in reward structure can lead to unintended consequences with potentially detrimental costs in real-world systems.', 'Discounting future rewards is a mathematical trick to prove and analyze certain aspects of convergence.', 'The reward is talked about using a discounted framework, valuing near-term rewards more than those farther into the future.', 'AI safety in robotics and autonomous vehicles, encoding subtle risks into AI-based control algorithms - 3', 'Addressing fundamental problems in AI safety as technology advances - 2', 'Unintended consequences and detrimental effects of AI, emphasizing the need for safety measures - 1', 'Challenges of reinforcement learning: defining environment, access space, reward structure - 4', 'Real-world impact requires collecting huge amounts of representative data, posing challenges - 5', 'Reinforcement learning applications: cart pole balancing, game playing, object manipulation, autonomous driving - 6', 'Model-based algorithms: sample efficiency, effective for reasoning and predicting future actions - 7', 'Value-based methods: estimate quality of states and actions, enabling off-policy learning - 8', 'Policy-based methods: directly learn a policy function, offering a different approach to learning optimal actions - 9', 'Q-learning: estimates value of actions in a state, aiding in maximizing rewards - 10', 'Exploration in Q-learning facilitated through epsilon-greedy strategy, gradually decreasing epsilon - 11', 'Traditional Q-table approach becomes impractical in real-world problems, leading to adoption of neural networks - 12', 'DQN achieves superhuman performance in arcade games with raw sensory pixels as input.', 'Experience replay and target network fixing significantly contribute to the stability and convergence of the training process.', 'Maintaining a fixed target network and updating it periodically mitigates stability challenges introduced by two Q functions in the loss function.', 'The DQN network achieves superhuman performance on various games.', 'The advantage measure in value-based optimization provides a better measure for choosing actions.', 'Dueling DQN decouples the quality of actions from the state.', 'Prioritized experience replay assigns priority based on the magnitude of the temporal difference learned error.', 'Policy gradient methods are capable of learning in messy environments and handling stochastic policies and continuous actions.', 'The Advantage Actor Critic methods combine the best of value-based and policy-based methods, allowing for more sample-efficient learning and are highly parallelizable.', 'The Deep Deterministic Policy Gradient method deals with continuous action spaces by using a deterministic policy with exploration through the addition of noise.', 'Careful policy optimization is crucial to prevent the collapse of training performance.', 'Alpha Zero outperforms state-of-the-art engines in Go, Shogi, and chess by exploring fewer branches and efficiently estimating the quality of game positions.', 'AlphaGo Zero achieved success through self-play without pre-training on expert systems, showcasing the effectiveness of neural networks in learning board positions through exploration.', "The use of Monte Carlo tree search in systems like AlphaGo involves balancing exploitation and exploration, demonstrating the neural network's role in estimating the potential success of different board positions.", 'The task for a neural network in games like Go is to learn the quality of the board and to determine which game positions are most useful to explore, leading to highly successful states.', 'Recent developments show a shift towards learning control dynamics through RL, aiming for more efficient and robust movement in robotics.', 'The exciting advancement in robotics involves using RL to teach robots to deal with unexpected initial conditions and perturbations, reducing the reliance on hard coding for such scenarios.', 'The use of reinforcement learning in driving policy to predict future driving behavior and anticipate intent modeling.', 'The transition from simulation to real world and the need for better transfer learning algorithms.', 'The idea of creating an infinite number of simulations for better transfer learning and regularization aspect for making reality another sample from those simulations.', 'Steps for research in RL include developing mathematical background, picking a framework, implementing core RL algorithms, and iterating fast on simple benchmark environments.']}