title
Deep Reinforcement Learning: Neural Networks for Learning Control Laws

description
Deep learning is enabling tremendous breakthroughs in the power of reinforcement learning for control. From games, like chess and alpha Go, to robotic systems, deep neural networks are providing a powerful and flexible representation framework that fits naturally with reinforcement learning. In this video, we provide an overview of developments in deep reinforcement learning, along with leading algorithms and impressive applications. Citable link for this video: https://doi.org/10.52843/cassyni.9tngmc @eigensteve on Twitter eigensteve.com databookuw.com

detail
{'title': 'Deep Reinforcement Learning: Neural Networks for Learning Control Laws', 'heatmap': [{'end': 153.05, 'start': 85.676, 'weight': 0.912}], 'summary': 'Explores deep reinforcement learning, covering advancements in leveraging deep neural networks for complex environment interaction, optimizing policies in a semi-supervised learning framework, discussing challenges and breakthroughs, and applications in real-world scenarios.', 'chapters': [{'end': 44.976, 'segs': [{'end': 44.976, 'src': 'embed', 'start': 7.244, 'weight': 0, 'content': [{'end': 7.704, 'text': 'Welcome back.', 'start': 7.244, 'duration': 0.46}, {'end': 12.828, 'text': "I'm Steve Brunton, and today I'm going to talk to you a bit more about reinforcement learning.", 'start': 8.305, 'duration': 4.523}, {'end': 21.974, 'text': 'So in the last video, I introduced the reinforcement learning architecture, how you can learn to interact with a complex environment from experience.', 'start': 13.048, 'duration': 8.926}, {'end': 33.863, 'text': "And today we're going to talk about deep reinforcement learning or some of the really amazing advances in this field that have been enabled by deep neural networks and these advanced computational architectures.", 'start': 22.595, 'duration': 11.268}, {'end': 38.288, 'text': 'So again, I am at EigenSteve on Twitter.', 'start': 34.683, 'duration': 3.605}, {'end': 44.976, 'text': 'Please do subscribe and do like, do share this if you find it useful and tell me other things that you would like me to talk about.', 'start': 38.428, 'duration': 6.548}], 'summary': "Steve brunton discusses deep reinforcement learning's advances enabled by deep neural networks and computational architectures.", 'duration': 37.732, 'max_score': 7.244, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/IUiKAD6cuTA/pics/IUiKAD6cuTA7244.jpg'}], 'start': 7.244, 'title': 'Deep reinforcement learning', 'summary': 'Delves into advancements in deep reinforcement learning, leveraging deep neural networks and computational architectures to learn interaction with complex environments, and encourages engagement on social media.', 'chapters': [{'end': 44.976, 'start': 7.244, 'title': 'Deep reinforcement learning', 'summary': 'Discusses the advances in deep reinforcement learning enabled by deep neural networks and computational architectures, emphasizing the ability to learn to interact with complex environments from experience, with a call to action for engagement on social media.', 'duration': 37.732, 'highlights': ['The chapter introduces the concept of reinforcement learning and its architecture for learning to interact with complex environments from experience.', 'Deep reinforcement learning is discussed, emphasizing the amazing advances enabled by deep neural networks and advanced computational architectures.', 'The speaker encourages engagement by inviting subscribers to like, share, and suggest topics on social media.']}], 'duration': 37.732, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/IUiKAD6cuTA/pics/IUiKAD6cuTA7244.jpg', 'highlights': ['Deep reinforcement learning leverages deep neural networks and advanced computational architectures for amazing advances.', 'The chapter introduces the concept of reinforcement learning and its architecture for learning to interact with complex environments from experience.', 'The speaker encourages engagement by inviting subscribers to like, share, and suggest topics on social media.']}, {'end': 324.997, 'segs': [{'end': 153.05, 'src': 'heatmap', 'start': 85.676, 'weight': 0.912, 'content': [{'end': 93.761, 'text': 'And there is a value function that tells the agent how valuable being in a given state is, given the policy pi that it is enacting.', 'start': 85.676, 'duration': 8.085}, {'end': 103.115, 'text': "And so today what we're going to do is we're going to augment this picture by introducing deep neural networks, for example, to represent the policy.", 'start': 94.511, 'duration': 8.604}, {'end': 108.537, 'text': "So here we have, now we've replaced our policy with a deep neural network.", 'start': 103.995, 'duration': 4.542}, {'end': 114.12, 'text': 'So this pi is parameterized by theta, where theta describes this neural network.', 'start': 108.797, 'duration': 5.323}, {'end': 121.143, 'text': 'And again, it maps the current state to the best probabilistic action to take in that environment.', 'start': 114.64, 'duration': 6.503}, {'end': 127.364, 'text': 'And so the whole name of the game is to update this policy to maximize future rewards.', 'start': 121.403, 'duration': 5.961}, {'end': 135.766, 'text': 'And again, we have this discount rate gamma here that says that rewards in the near future are worth more than rewards in the distant future.', 'start': 127.624, 'duration': 8.142}, {'end': 141.147, 'text': 'Because, again remember, these rewards are going to be relatively sparse and infrequent,', 'start': 136.746, 'duration': 4.401}, {'end': 153.05, 'text': "most of the time because we're in a semi-supervised learning framework where these rewards are only occasional and so it's difficult to figure out what actions actually gave rise to those rewards.", 'start': 141.147, 'duration': 11.903}], 'summary': 'Introducing deep neural networks to update policy and maximize future rewards in reinforcement learning.', 'duration': 67.374, 'max_score': 85.676, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/IUiKAD6cuTA/pics/IUiKAD6cuTA85676.jpg'}, {'end': 121.143, 'src': 'embed', 'start': 94.511, 'weight': 0, 'content': [{'end': 103.115, 'text': "And so today what we're going to do is we're going to augment this picture by introducing deep neural networks, for example, to represent the policy.", 'start': 94.511, 'duration': 8.604}, {'end': 108.537, 'text': "So here we have, now we've replaced our policy with a deep neural network.", 'start': 103.995, 'duration': 4.542}, {'end': 114.12, 'text': 'So this pi is parameterized by theta, where theta describes this neural network.', 'start': 108.797, 'duration': 5.323}, {'end': 121.143, 'text': 'And again, it maps the current state to the best probabilistic action to take in that environment.', 'start': 114.64, 'duration': 6.503}], 'summary': 'Introducing deep neural networks to represent the policy for mapping current state to best probabilistic action.', 'duration': 26.632, 'max_score': 94.511, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/IUiKAD6cuTA/pics/IUiKAD6cuTA94511.jpg'}, {'end': 186.545, 'src': 'embed', 'start': 160.473, 'weight': 4, 'content': [{'end': 165.556, 'text': 'But, you know, and actually the whole reinforcement learning paradigm is biologically inspired.', 'start': 160.473, 'duration': 5.083}, {'end': 169.097, 'text': "It's essentially inspired by this observation.", 'start': 165.616, 'duration': 3.481}, {'end': 172.418, 'text': "So there's this notion called Hebbian learning.", 'start': 169.157, 'duration': 3.261}, {'end': 173.439, 'text': 'You might have heard this before.', 'start': 172.458, 'duration': 0.981}, {'end': 180.042, 'text': 'And the little rhyme goes, neurons that fire together wire together.', 'start': 174.539, 'duration': 5.503}, {'end': 186.545, 'text': 'And basically what that means is that when you have neural activity, kind of when things fire together,', 'start': 180.682, 'duration': 5.863}], 'summary': 'Reinforcement learning is inspired by hebbian learning, where neurons that fire together wire together.', 'duration': 26.072, 'max_score': 160.473, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/IUiKAD6cuTA/pics/IUiKAD6cuTA160473.jpg'}, {'end': 222.546, 'src': 'embed', 'start': 195.93, 'weight': 3, 'content': [{'end': 205.036, 'text': 'the idea is that the reward signal that you get occasionally should somehow strengthen connections that led to a good policy.', 'start': 195.93, 'duration': 9.106}, {'end': 212.281, 'text': 'When the right policy is firing, when these neurons are connected in a way that causes the correct policy and you get a reward.', 'start': 205.096, 'duration': 7.185}, {'end': 215.623, 'text': 'you want to somehow reinforce that architecture.', 'start': 212.281, 'duration': 3.342}, {'end': 222.546, 'text': "And there's lots of ways of doing this, you know, essentially through back propagation and so on and so forth.", 'start': 216.443, 'duration': 6.103}], 'summary': 'Strengthen connections for good policy with occasional reward signal, using back propagation.', 'duration': 26.616, 'max_score': 195.93, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/IUiKAD6cuTA/pics/IUiKAD6cuTA195930.jpg'}, {'end': 262.938, 'src': 'embed', 'start': 238.293, 'weight': 2, 'content': [{'end': 244.796, 'text': 'and it tells you jointly how good is a current state, given a current action A?', 'start': 238.293, 'duration': 6.503}, {'end': 250.002, 'text': 'Okay, and so assuming that I do the best possible thing for all future states and actions.', 'start': 245.496, 'duration': 4.506}, {'end': 259.553, 'text': 'So right now, if I find myself in state S and action A, I can assign a quality based on the future value that I expect.', 'start': 250.523, 'duration': 9.03}, {'end': 262.938, 'text': 'given that state and given the best possible policy, I can cook up.', 'start': 259.553, 'duration': 3.385}], 'summary': 'Determining the quality of a current state and action based on future value and best possible policy.', 'duration': 24.645, 'max_score': 238.293, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/IUiKAD6cuTA/pics/IUiKAD6cuTA238293.jpg'}, {'end': 330.039, 'src': 'embed', 'start': 304.409, 'weight': 1, 'content': [{'end': 312.072, 'text': 'So it kind of makes sense that this would be an area for really expanding with deep neural networks, because these functions might be very,', 'start': 304.409, 'duration': 7.663}, {'end': 312.892, 'text': 'very complex.', 'start': 312.072, 'duration': 0.82}, {'end': 319.675, 'text': "functions of S and A, and that's exactly what neural networks are good at is giving you very, very complex,", 'start': 312.892, 'duration': 6.783}, {'end': 322.756, 'text': 'representing very complex functions if you have enough training data.', 'start': 319.675, 'duration': 3.081}, {'end': 324.997, 'text': "So, that's what we're talking about here.", 'start': 323.436, 'duration': 1.561}, {'end': 330.039, 'text': 'These still suffer from all of the same challenges of regular reinforcement learning.', 'start': 325.417, 'duration': 4.622}], 'summary': 'Neural networks excel at representing complex functions with sufficient training data in the context of reinforcement learning.', 'duration': 25.63, 'max_score': 304.409, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/IUiKAD6cuTA/pics/IUiKAD6cuTA304409.jpg'}], 'start': 46.078, 'title': 'Reinforcement learning and biological inspiration', 'summary': 'Introduces reinforcement learning with deep neural networks, optimizing the policy in a semi-supervised learning framework, and explores biological inspiration, including hebbian learning and the use of q-learning with a focus on deep q networks.', 'chapters': [{'end': 159.793, 'start': 46.078, 'title': 'Reinforcement learning with deep neural networks', 'summary': 'Introduces the concept of reinforcement learning where an agent interacts with an environment through a probabilistic policy, aiming to maximize future rewards, and discusses the incorporation of deep neural networks to represent the policy and the challenge of optimizing the policy in a semi-supervised learning framework with sparse rewards.', 'duration': 113.715, 'highlights': ['The agent interacts with the environment through a probabilistic policy to maximize future rewards. The agent measures the environment through the state S, takes actions based on a control policy, and aims to optimize the policy to maximize future rewards.', 'Introduction of deep neural networks to represent the policy and the challenge of optimizing the policy in a semi-supervised learning framework with sparse rewards. The chapter discusses the augmentation of the existing picture by introducing deep neural networks to represent the policy and highlights the challenge of optimizing the policy in a semi-supervised learning framework with sparse and infrequent rewards.', 'Incorporation of the discount rate gamma to prioritize near-future rewards over distant future rewards due to the sparse and infrequent nature of rewards. The discount rate gamma is introduced to emphasize that rewards in the near future are worth more than rewards in the distant future, considering the sparse and infrequent nature of rewards in the semi-supervised learning framework.']}, {'end': 324.997, 'start': 160.473, 'title': 'Biological inspiration for deep reinforcement learning', 'summary': 'Explains the biological inspiration for reinforcement learning, particularly hebbian learning and the reinforcement of connections between neurons, as well as the use of q-learning to determine the quality of current state-action pairs, with a focus on deep q networks.', 'duration': 164.524, 'highlights': ['The concept of Hebbian learning, where neural activity strengthens the wiring and connections between neurons, forms the biological inspiration for reinforcement learning. Hebbian learning strengthens connections between neurons when they fire together, forming the basis for reinforcement in biological systems.', 'The reinforcement of connections that led to a good policy is achieved through the reward signal in deep reinforcement learning architectures. The reward signal strengthens connections associated with a good policy, contributing to the reinforcement of the architecture in deep reinforcement learning.', 'Q-learning involves determining the quality of a current state-action pair by combining the policy and value functions, enabling the selection of the best action for a given state. Q-learning combines the policy and value functions to assess the quality of a current state-action pair, facilitating the selection of the best action for a given state.', 'Deep Q networks learn the quality function, allowing for the determination of the best possible action for a given state, resembling how a person learns to play chess by simultaneously building a policy and a value function. Deep Q networks learn the quality function to identify the best action for a given state, mirroring the process of simultaneously building a policy and a value function, akin to learning to play chess.', 'Deep neural networks are well-suited for expanding in the area of reinforcement learning due to their capability to represent complex functions of state and action. Deep neural networks excel in representing complex functions of state and action, making them suitable for expanding in the field of reinforcement learning.']}], 'duration': 278.919, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/IUiKAD6cuTA/pics/IUiKAD6cuTA46078.jpg', 'highlights': ['Introduction of deep neural networks to represent the policy and the challenge of optimizing the policy in a semi-supervised learning framework with sparse rewards.', 'Deep neural networks are well-suited for expanding in the area of reinforcement learning due to their capability to represent complex functions of state and action.', 'Q-learning combines the policy and value functions to assess the quality of a current state-action pair, facilitating the selection of the best action for a given state.', 'The reward signal strengthens connections associated with a good policy, contributing to the reinforcement of the architecture in deep reinforcement learning.', 'The concept of Hebbian learning strengthens connections between neurons when they fire together, forming the basis for reinforcement in biological systems.']}, {'end': 605.528, 'segs': [{'end': 351.243, 'src': 'embed', 'start': 325.417, 'weight': 2, 'content': [{'end': 330.039, 'text': 'These still suffer from all of the same challenges of regular reinforcement learning.', 'start': 325.417, 'duration': 4.622}, {'end': 332.279, 'text': 'like the credit assignment problem.', 'start': 330.899, 'duration': 1.38}, {'end': 340.961, 'text': 'So the fact that I might only get a reward at the very end of my chess game makes it very hard to tell which actions actually gave rise to that reward.', 'start': 332.759, 'duration': 8.202}, {'end': 345.202, 'text': "And so you're going to do some of the same things that you would normally do,", 'start': 341.601, 'duration': 3.601}, {'end': 351.243, 'text': 'like you might use reward shaping to give intermediate rewards based on some expert intuition or guidance.', 'start': 345.202, 'duration': 6.041}], 'summary': 'Reinforcement learning faces challenges like credit assignment problem, requiring reward shaping for intermediate rewards.', 'duration': 25.826, 'max_score': 325.417, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/IUiKAD6cuTA/pics/IUiKAD6cuTA325417.jpg'}, {'end': 391.078, 'src': 'embed', 'start': 369.445, 'weight': 0, 'content': [{'end': 379.051, 'text': 'Good And all of this kind of exploded on the scene because of this 2015 Nature paper Human Level Control Through Deep Reinforcement Learning,', 'start': 369.445, 'duration': 9.606}, {'end': 389.317, 'text': 'where these authors from DeepMind essentially showed that they could build a reinforcement learner that could beat human level performance in lots of classic Atari video games.', 'start': 379.051, 'duration': 10.266}, {'end': 390.518, 'text': "So I'm going to hit play.", 'start': 389.337, 'duration': 1.181}, {'end': 391.078, 'text': 'I love this one.', 'start': 390.558, 'duration': 0.52}], 'summary': 'In 2015, deepmind demonstrated a reinforcement learner beating human level performance in classic atari video games.', 'duration': 21.633, 'max_score': 369.445, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/IUiKAD6cuTA/pics/IUiKAD6cuTA369445.jpg'}, {'end': 436.923, 'src': 'embed', 'start': 411.722, 'weight': 4, 'content': [{'end': 418.027, 'text': "So it essentially finds an exploit in the game where it realizes that if it tunnels in one side, so it's going to tunnel through here.", 'start': 411.722, 'duration': 6.305}, {'end': 425.714, 'text': 'If it tunnels through one side, it can essentially use the physics of the game to break all of these blocks for it.', 'start': 419.028, 'duration': 6.686}, {'end': 427.655, 'text': "And that's pretty amazing.", 'start': 426.734, 'duration': 0.921}, {'end': 435.281, 'text': 'So it in a short amount of time learns a really advanced strategy that only a few humans, only a small percentage of humans,', 'start': 427.715, 'duration': 7.566}, {'end': 436.923, 'text': 'would actually learn eventually.', 'start': 435.281, 'duration': 1.642}], 'summary': 'An ai learns an advanced game strategy that few humans can master.', 'duration': 25.201, 'max_score': 411.722, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/IUiKAD6cuTA/pics/IUiKAD6cuTA411722.jpg'}, {'end': 495.366, 'src': 'embed', 'start': 469.114, 'weight': 3, 'content': [{'end': 473.056, 'text': "because this showed performance that hadn't been attainable before.", 'start': 469.114, 'duration': 3.942}, {'end': 476.918, 'text': 'So this is a lot like the ImageNet of reinforcement learning.', 'start': 473.116, 'duration': 3.802}, {'end': 478.939, 'text': 'This brought it back into the forefront.', 'start': 477.279, 'duration': 1.66}, {'end': 480.5, 'text': 'Since then.', 'start': 479.94, 'duration': 0.56}, {'end': 483.802, 'text': 'so Google bought this company for half a billion dollars,', 'start': 480.5, 'duration': 3.302}, {'end': 495.366, 'text': 'because this was promised a big step towards general artificial intelligence or an artificial intelligence system that could get good at lots of things rather than just one very specific task.', 'start': 483.802, 'duration': 11.564}], 'summary': 'Google acquired the company for half a billion dollars, marking a significant advance in reinforcement learning.', 'duration': 26.252, 'max_score': 469.114, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/IUiKAD6cuTA/pics/IUiKAD6cuTA469114.jpg'}, {'end': 592.638, 'src': 'embed', 'start': 564.981, 'weight': 5, 'content': [{'end': 572.145, 'text': "It's one of the big, big problems in the field is kind of transfer learning and general artificial intelligence using reinforcement learning.", 'start': 564.981, 'duration': 7.164}, {'end': 577.007, 'text': 'So building a learner that can learn lots of things and learn faster from its experience.', 'start': 572.185, 'duration': 4.822}, {'end': 578.228, 'text': "That's what humans do.", 'start': 577.487, 'duration': 0.741}, {'end': 581.29, 'text': 'You get a kid, you teach them tic-tac-toe.', 'start': 579.148, 'duration': 2.142}, {'end': 582.49, 'text': 'Tic-tac-toe is easy.', 'start': 581.53, 'duration': 0.96}, {'end': 583.591, 'text': 'They learn the rules.', 'start': 582.651, 'duration': 0.94}, {'end': 584.992, 'text': 'They learn how to not lose.', 'start': 583.651, 'duration': 1.341}, {'end': 586.553, 'text': 'Then you give them checkers.', 'start': 585.433, 'duration': 1.12}, {'end': 592.638, 'text': 'Checkers is a little bit more sophisticated, but they remember everything they learned from tic-tac-toe and they learn checkers faster.', 'start': 587.154, 'duration': 5.484}], 'summary': 'Transfer learning in ai aims to enable faster learning from experience, akin to human learning process.', 'duration': 27.657, 'max_score': 564.981, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/IUiKAD6cuTA/pics/IUiKAD6cuTA564981.jpg'}], 'start': 325.417, 'title': 'Deep reinforcement learning breakthroughs', 'summary': "Discusses challenges in reinforcement learning, including credit assignment, reward shaping, and hindsight replay. it highlights the 2015 nature paper showcasing human-level performance in atari games. additionally, it covers deepmind's breakthrough in mastering atari games at or above human level, leading to significant investment and research in deep reinforcement learning, while highlighting challenges in transfer learning and achieving general artificial intelligence.", 'chapters': [{'end': 427.655, 'start': 325.417, 'title': 'Deep reinforcement learning', 'summary': 'Discusses the challenges of reinforcement learning, including the credit assignment problem and strategies like reward shaping and hindsight replay. it also highlights the breakthrough in deep reinforcement learning with a nature paper in 2015 showcasing a reinforcement learner achieving human-level performance in classic atari video games.', 'duration': 102.238, 'highlights': ['The Nature paper in 2015 demonstrated a reinforcement learner achieving human-level performance in classic Atari video games, which sparked excitement in the field. (Relevance: 5)', 'Reinforcement learning faces challenges such as the credit assignment problem, making it difficult to determine which actions lead to rewards, thus requiring strategies like reward shaping and hindsight replay. (Relevance: 4)', "The reinforcement learner, after a few hours of training, discovered an exploit in the game to maximize its score by tunneling through one side and using the game's physics to break all the blocks. (Relevance: 3)"]}, {'end': 605.528, 'start': 427.715, 'title': "Deepmind's reinforcement learning breakthrough", 'summary': "Discusses deepmind's reinforcement learning breakthrough in mastering atari games at or above human level, leading to significant investment and research in deep reinforcement learning, but highlights challenges in transfer learning and achieving general artificial intelligence.", 'duration': 177.813, 'highlights': ["DeepMind's algorithm achieved human-level or above performance in most Atari games, leading to a significant breakthrough in reinforcement learning. The algorithm achieved human-level or above performance in most Atari games, signaling a significant breakthrough in reinforcement learning.", "Google's acquisition of DeepMind for half a billion dollars and subsequent multi-billion dollar investment in reinforcement learning underscore the impact and potential of the breakthrough. Google acquired DeepMind for half a billion dollars, signaling the significant impact and potential of the breakthrough, leading to multi-billion dollar investment in reinforcement learning.", 'Challenges in transfer learning and achieving general artificial intelligence using reinforcement learning are highlighted, as the current algorithm cannot be easily transferred to play different games without complete retraining. The current algorithm faces challenges in transfer learning and achieving general artificial intelligence using reinforcement learning, as it requires complete retraining to play different games.']}], 'duration': 280.111, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/IUiKAD6cuTA/pics/IUiKAD6cuTA325417.jpg', 'highlights': ['The Nature paper in 2015 demonstrated a reinforcement learner achieving human-level performance in classic Atari video games, sparking excitement in the field.', "DeepMind's algorithm achieved human-level or above performance in most Atari games, signaling a significant breakthrough in reinforcement learning.", 'Reinforcement learning faces challenges such as the credit assignment problem, making it difficult to determine which actions lead to rewards, thus requiring strategies like reward shaping and hindsight replay.', 'Google acquired DeepMind for half a billion dollars, signaling the significant impact and potential of the breakthrough, leading to multi-billion dollar investment in reinforcement learning.', "The reinforcement learner, after a few hours of training, discovered an exploit in the game to maximize its score by tunneling through one side and using the game's physics to break all the blocks.", 'Challenges in transfer learning and achieving general artificial intelligence using reinforcement learning are highlighted, as the current algorithm requires complete retraining to play different games.']}, {'end': 1014.095, 'segs': [{'end': 672.261, 'src': 'embed', 'start': 646.704, 'weight': 0, 'content': [{'end': 652.988, 'text': 'Our bodies are built to move very efficiently and agile and accurate ways.', 'start': 646.704, 'duration': 6.284}, {'end': 655.87, 'text': 'But this is actually very challenging to do.', 'start': 653.909, 'duration': 1.961}, {'end': 665.316, 'text': 'And so the fact that these algorithms in a virtual environment can learn how to run and walk and fly and swim is really promising for robotic technology.', 'start': 656.09, 'duration': 9.226}, {'end': 672.261, 'text': 'So we eventually want to learn how to do this in robotic systems and make our robotic agents more independent.', 'start': 665.376, 'duration': 6.885}], 'summary': 'Algorithms in virtual environment learn to move efficiently, promising for robotic technology.', 'duration': 25.557, 'max_score': 646.704, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/IUiKAD6cuTA/pics/IUiKAD6cuTA646704.jpg'}, {'end': 805.627, 'src': 'embed', 'start': 757.739, 'weight': 1, 'content': [{'end': 766.31, 'text': "These can represent functions that we previously couldn't represent because they're extremely expressive and we have lots more training data.", 'start': 757.739, 'duration': 8.571}, {'end': 770.858, 'text': 'We can train these because our computers have gotten so much faster and more powerful.', 'start': 767.171, 'duration': 3.687}, {'end': 778.672, 'text': "And there's also open source software that makes it really, really easy to get started building these neural network representations.", 'start': 771.88, 'duration': 6.792}, {'end': 785.662, 'text': 'And so, if you have any interest at all in modern reinforcement learning, you have to check out OpenAI Gym.', 'start': 779.68, 'duration': 5.982}, {'end': 799.365, 'text': 'This is a wonderful open source kind of development framework where you can try out your new reinforcement learning algorithm on all of these different systems both Atari games,', 'start': 786.142, 'duration': 13.223}, {'end': 805.627, 'text': 'simulated running and Pendula, and really cool physical systems that are hard to control, that are non-linear.', 'start': 799.365, 'duration': 6.262}], 'summary': 'Neural networks are now more expressive due to increased training data, faster computers, and open source software, making it easier to build modern reinforcement learning algorithms like those in openai gym.', 'duration': 47.888, 'max_score': 757.739, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/IUiKAD6cuTA/pics/IUiKAD6cuTA757739.jpg'}, {'end': 875.995, 'src': 'embed', 'start': 846.762, 'weight': 4, 'content': [{'end': 850.544, 'text': 'The best Go player in the world was defeated by AlphaGo.', 'start': 846.762, 'duration': 3.782}, {'end': 856.348, 'text': 'This was a deep reinforcement learning algorithm developed at Google DeepMind.', 'start': 851.464, 'duration': 4.884}, {'end': 859.649, 'text': 'with the sole purpose of learning how to play Go.', 'start': 857.228, 'duration': 2.421}, {'end': 875.995, 'text': "And so I want to point out that reinforcement learning is really good at learning the rules of the game and how to win the game when it's very constrained and when it has all the time in the world to try millions or billions of different Go games.", 'start': 860.269, 'duration': 15.726}], 'summary': "Alphago defeated the best go player, showcasing deep reinforcement learning's capability to excel in constrained environments and learn game rules.", 'duration': 29.233, 'max_score': 846.762, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/IUiKAD6cuTA/pics/IUiKAD6cuTA846762.jpg'}, {'end': 957.035, 'src': 'embed', 'start': 922.228, 'weight': 5, 'content': [{'end': 925.588, 'text': "I think there's a documentary on this that's really interesting, so you should check it out.", 'start': 922.228, 'duration': 3.36}, {'end': 937.851, 'text': 'The original AlphaGo, algorithm was based on a convolutional neural network, a CNN, and it had lots of reward shaping from humans.', 'start': 927.029, 'duration': 10.822}, {'end': 943.212, 'text': 'So expert humans guided the reward structure for this AlphaGo learner.', 'start': 938.031, 'duration': 5.181}, {'end': 948.613, 'text': "So it didn't have to wait until the end of the game to figure out if it won or lost, because that would take forever.", 'start': 943.292, 'duration': 5.321}, {'end': 957.035, 'text': 'Instead, humans using reward shaping helped it to get intermediate rewards to help it to learn faster and give it a denser reward structure.', 'start': 949.413, 'duration': 7.622}], 'summary': 'Alphago algorithm used cnn and reward shaping by humans for faster learning.', 'duration': 34.807, 'max_score': 922.228, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/IUiKAD6cuTA/pics/IUiKAD6cuTA922228.jpg'}, {'end': 999.044, 'src': 'embed', 'start': 965.903, 'weight': 3, 'content': [{'end': 968.365, 'text': "because it's fundamentally relying on human knowledge.", 'start': 965.903, 'duration': 2.462}, {'end': 976.554, 'text': 'So the next generation, AlphaGo Zero, which came a couple of years later, was even better and much more impressive.', 'start': 969.206, 'duration': 7.348}, {'end': 979.597, 'text': "It didn't use any human features, no reward shaping.", 'start': 976.714, 'duration': 2.883}, {'end': 982.278, 'text': 'It only learned using self-play.', 'start': 980.217, 'duration': 2.061}, {'end': 988.06, 'text': 'It just played itself until it became so powerful that it could beat everyone in the world, including the original one.', 'start': 982.318, 'duration': 5.742}, {'end': 999.044, 'text': 'And it was based on a residual network architecture that had these jump connections and are easier to train with backpropagation.', 'start': 988.08, 'duration': 10.964}], 'summary': 'Alphago zero surpassed original using self-play, residual network architecture, and no human features.', 'duration': 33.141, 'max_score': 965.903, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/IUiKAD6cuTA/pics/IUiKAD6cuTA965903.jpg'}], 'start': 606.509, 'title': 'Reinforcement learning advancements', 'summary': "Discusses recent advances in reinforcement learning, such as training algorithms for complex tasks and open source software, as well as the significant advancements in alphago, including defeating the world's best go player and achieving mastery through self-play and novel neural network architecture.", 'chapters': [{'end': 819.558, 'start': 606.509, 'title': 'Advances in reinforcement learning', 'summary': 'Discusses the recent advances in reinforcement learning, particularly in training algorithms to perform complex tasks in virtual environments and the development of open source software that facilitates rapid prototyping of reinforcement learning algorithms.', 'duration': 213.049, 'highlights': ['Recent advances in training algorithms to perform complex tasks in virtual environments, such as walking, running, swimming, and flying, using reinforcement learning are promising for robotic technology. The reinforcement learning algorithms are used to train agents to perform complex tasks in artificial environments, such as walking, running, swimming, and flying, which is promising for the development of more independent and agile robotic agents.', 'The representational power of neural networks has significantly advanced in the last 10 years, allowing the representation of functions that were previously impossible, facilitated by the availability of more training data and faster, more powerful computers. The significant advances in the representational power of neural networks have enabled the representation of previously impossible functions due to the availability of more training data and faster, more powerful computers.', 'The development of OpenAI Gym provides an open source framework for rapid prototyping of reinforcement learning algorithms on various systems, contributing to the rapid growth of reinforcement learning. OpenAI Gym offers an open source development framework for trying out reinforcement learning algorithms on diverse systems, including Atari games and physical systems, facilitating rapid prototyping and contributing to the rapid growth of reinforcement learning.']}, {'end': 1014.095, 'start': 819.558, 'title': 'Reinforcement learning in alphago', 'summary': "Discusses the significant advancements in reinforcement learning through the example of alphago, which defeated the world's best go player with a deep reinforcement learning algorithm, and the subsequent advancements in alphago zero which achieved mastery through self-play and a novel neural network architecture.", 'duration': 194.537, 'highlights': ['AlphaGo Zero achieved mastery through self-play and a novel neural network architecture AlphaGo Zero, developed two years after the original AlphaGo, did not use any human features or reward shaping and only learned through self-play. It utilized a residual network architecture with jump connections and demonstrated that ResNet was a major contender in the neural network architecture scene.', "AlphaGo defeated the world's best Go player using deep reinforcement learning algorithm AlphaGo, developed by Google DeepMind, defeated the world's best Go player, Lee Sedol, with a deep reinforcement learning algorithm, showcasing the capability of reinforcement learning to learn the rules and win the game when constrained and given ample time to try different game scenarios.", "Reward shaping from humans guided the original AlphaGo's learning The original AlphaGo algorithm, based on a convolutional neural network, utilized reward shaping from expert humans to provide intermediate rewards, enabling faster learning and a denser reward structure, although it relied fundamentally on human knowledge."]}], 'duration': 407.586, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/IUiKAD6cuTA/pics/IUiKAD6cuTA606509.jpg', 'highlights': ['Recent advances in training algorithms for complex tasks in virtual environments using reinforcement learning are promising for robotic technology.', 'The representational power of neural networks has significantly advanced in the last 10 years, enabling the representation of previously impossible functions.', 'OpenAI Gym provides an open source framework for rapid prototyping of reinforcement learning algorithms on various systems, contributing to the rapid growth of reinforcement learning.', 'AlphaGo Zero achieved mastery through self-play and a novel neural network architecture, demonstrating the potential of self-learning systems.', "AlphaGo defeated the world's best Go player using a deep reinforcement learning algorithm, showcasing the capability of reinforcement learning to learn and win the game.", 'The original AlphaGo algorithm utilized reward shaping from expert humans to provide intermediate rewards, enabling faster learning and a denser reward structure.']}, {'end': 1273.302, 'segs': [{'end': 1060.183, 'src': 'embed', 'start': 1032.391, 'weight': 1, 'content': [{'end': 1035.874, 'text': "That's what we do and that's really fun and, you know, that's one of our strengths.", 'start': 1032.391, 'duration': 3.483}, {'end': 1039.459, 'text': 'So some other examples I love.', 'start': 1037.136, 'duration': 2.323}, {'end': 1048.791, 'text': 'this is a video from Stanford and from ETH Zurich, where they are essentially going to train using reinforcement learning,', 'start': 1039.459, 'duration': 9.332}, {'end': 1053.958, 'text': 'flying uninhabited aerial vehicles, a helicopter and a quadrotor.', 'start': 1048.791, 'duration': 5.167}, {'end': 1060.183, 'text': 'So they can train these very aggressive, very high performance maneuvers using reinforcement learning.', 'start': 1054.158, 'duration': 6.025}], 'summary': 'Training uninhabited aerial vehicles using reinforcement learning at stanford and eth zurich.', 'duration': 27.792, 'max_score': 1032.391, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/IUiKAD6cuTA/pics/IUiKAD6cuTA1032391.jpg'}, {'end': 1131.727, 'src': 'embed', 'start': 1103.8, 'weight': 2, 'content': [{'end': 1111.082, 'text': 'It turns out that in a really, really big building like a super skyscraper that has tons of elevators and lots of floors,', 'start': 1103.8, 'duration': 7.282}, {'end': 1119.603, 'text': "scheduling these things efficiently so that you don't get jammed up and so that people can get where they're going as fast as possible is a huge problem.", 'start': 1111.082, 'duration': 8.521}, {'end': 1123.484, 'text': "It's a combinatorially hard problem and it's really hard to solve,", 'start': 1119.723, 'duration': 3.761}, {'end': 1131.727, 'text': 'and reinforcement learning was one of the early algorithms that was used to kind of figure out this near optimal scheduling policy.', 'start': 1123.964, 'duration': 7.763}], 'summary': 'Efficiently scheduling elevators in skyscrapers is a complex problem, tackled by reinforcement learning for near-optimal solutions.', 'duration': 27.927, 'max_score': 1103.8, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/IUiKAD6cuTA/pics/IUiKAD6cuTA1103800.jpg'}, {'end': 1188.871, 'src': 'embed', 'start': 1164.535, 'weight': 0, 'content': [{'end': 1174.181, 'text': 'And in the last 10 years because of advances in deep neural network and major advances in this architecture and how we actually build and optimize these reinforcement learners.', 'start': 1164.535, 'duration': 9.646}, {'end': 1182.447, 'text': 'there have been big steps towards more powerful, more general learning frameworks that can learn how to interact with much more complex environments,', 'start': 1174.181, 'duration': 8.266}, {'end': 1188.871, 'text': 'like beating humans at AlphaGo or actually moving real robotic systems in really incredible ways.', 'start': 1182.447, 'duration': 6.424}], 'summary': 'Advances in deep neural networks have led to more powerful learning frameworks, achieving significant milestones like beating humans at alphago and moving real robotic systems.', 'duration': 24.336, 'max_score': 1164.535, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/IUiKAD6cuTA/pics/IUiKAD6cuTA1164535.jpg'}, {'end': 1237.052, 'src': 'embed', 'start': 1207.421, 'weight': 3, 'content': [{'end': 1213.344, 'text': 'And when they learn one thing, they can use it through this incredible human power of abstraction.', 'start': 1207.421, 'duration': 5.923}, {'end': 1217.026, 'text': 'They can use what they learn in one scenario in a totally different scenario.', 'start': 1213.704, 'duration': 3.322}, {'end': 1221.067, 'text': 'That is still a completely open problem in reinforcement learning.', 'start': 1217.666, 'duration': 3.401}, {'end': 1222.548, 'text': 'Maybe not completely open.', 'start': 1221.467, 'duration': 1.081}, {'end': 1226.249, 'text': 'It is a pressing and central challenge in modern reinforcement.', 'start': 1222.628, 'duration': 3.621}, {'end': 1229.65, 'text': 'learning is how to take what you learn and generalize,', 'start': 1226.249, 'duration': 3.401}, {'end': 1237.052, 'text': 'how to take a step back and use your expertise in one problem in one environment to solve another problem in another environment.', 'start': 1229.65, 'duration': 7.402}], 'summary': 'Reinforcement learning faces challenge of generalization in different scenarios.', 'duration': 29.631, 'max_score': 1207.421, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/IUiKAD6cuTA/pics/IUiKAD6cuTA1207421.jpg'}], 'start': 1014.856, 'title': 'Reinforcement learning applications and advancements', 'summary': 'Discusses the application of reinforcement learning in real-world scenarios, such as training aerial vehicles and elevator scheduling, along with advancements driven by deep neural networks and the challenges of generalizing knowledge across different environments.', 'chapters': [{'end': 1123.484, 'start': 1014.856, 'title': 'Reinforcement learning in real world', 'summary': 'Discusses the application of reinforcement learning in real-world scenarios, such as training aerial vehicles and elevator scheduling, highlighting the challenges and limited examples of real robotic reinforcement learning.', 'duration': 108.628, 'highlights': ['Training aerial vehicles using reinforcement learning The chapter discusses the use of reinforcement learning to train flying uninhabited aerial vehicles, such as helicopters and quadrotors, for aggressive and high-performance maneuvers.', 'Challenges in transitioning from simulated environments to real robotic systems It is noted that transitioning from simulated environments to real-world robotic systems poses significant challenges, requiring extensive training and human guidance.', 'Application of reinforcement learning in elevator scheduling The chapter highlights the surprising application of reinforcement learning in elevator scheduling for efficiently managing elevators in large buildings, addressing the combinatorially hard problem of optimizing travel times.']}, {'end': 1273.302, 'start': 1123.964, 'title': 'Reinforcement learning advancements', 'summary': 'Discusses the advancements in reinforcement learning driven by deep neural networks and the challenges of generalizing knowledge across different environments, posing a long-term research opportunity.', 'duration': 149.338, 'highlights': ['Advances in deep neural network and architecture have led to more powerful and general learning frameworks in reinforcement learning, enabling complex interactions with environments, such as beating humans at AlphaGo and controlling robotic systems.', 'The pressing challenge in modern reinforcement learning is to generalize knowledge across different environments, which remains a central problem in achieving real general artificial intelligence.', 'The field of reinforcement learning offers a long-term opportunity for important and interesting research in improving learning systems and generalizing knowledge across different problems.', 'Reinforcement learning is fundamentally biologically inspired, aiming to mimic how animals learn and how humans interact with their environment.']}], 'duration': 258.446, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/IUiKAD6cuTA/pics/IUiKAD6cuTA1014856.jpg', 'highlights': ['Advances in deep neural network and architecture have led to more powerful and general learning frameworks in reinforcement learning, enabling complex interactions with environments, such as beating humans at AlphaGo and controlling robotic systems.', 'The chapter discusses the use of reinforcement learning to train flying uninhabited aerial vehicles, such as helicopters and quadrotors, for aggressive and high-performance maneuvers.', 'The chapter highlights the surprising application of reinforcement learning in elevator scheduling for efficiently managing elevators in large buildings, addressing the combinatorially hard problem of optimizing travel times.', 'The pressing challenge in modern reinforcement learning is to generalize knowledge across different environments, which remains a central problem in achieving real general artificial intelligence.']}], 'highlights': ['Deep reinforcement learning leverages deep neural networks and advanced computational architectures for amazing advances.', 'Introduction of deep neural networks to represent the policy and the challenge of optimizing the policy in a semi-supervised learning framework with sparse rewards.', 'The Nature paper in 2015 demonstrated a reinforcement learner achieving human-level performance in classic Atari video games, sparking excitement in the field.', 'Recent advances in training algorithms for complex tasks in virtual environments using reinforcement learning are promising for robotic technology.', 'Advances in deep neural network and architecture have led to more powerful and general learning frameworks in reinforcement learning, enabling complex interactions with environments, such as beating humans at AlphaGo and controlling robotic systems.']}