title
MIT 6.S191: Reinforcement Learning

description
MIT Introduction to Deep Learning 6.S191: Lecture 5 Deep Reinforcement Learning Lecturer: Alexander Amini 2023 Edition For all lectures, slides, and lab materials: http://introtodeeplearning.com Lecture Outline: 0:00 - Introduction 3:49 - Classes of learning problems 6:48 - Definitions 12:24 - The Q function 17:06 - Deeper into the Q function 21:32 - Deep Q Networks 29:15 - Atari results and limitations 32:42 - Policy learning algorithms 36:42 - Discrete vs continuous actions 39:48 - Training policy gradients 47:17 - RL in real life 49:55 - VISTA simulator 52:04 - AlphaGo and AlphaZero and MuZero 56:34 - Summary Subscribe to stay up to date with new deep learning lectures at MIT, or follow us @MITDeepLearning on Twitter and Instagram to stay fully-connected!!

detail
{'title': 'MIT 6.S191: Reinforcement Learning', 'heatmap': [{'end': 1527.226, 'start': 1446.084, 'weight': 0.891}], 'summary': 'Covers integrating reinforcement learning and deep learning for real-world deployment, discussing key components and applications, such as robotics, autonomy, and ai competition in starcraft. it also delves into q-learning, policy learning, atari breakout, optimizing agent performance, deep q-learning, breakthroughs in q-learning and policy learning, and training neural networks with policy gradient algorithm for reinforcement learning applications.', 'chapters': [{'end': 226.842, 'segs': [{'end': 53.742, 'src': 'embed', 'start': 25.27, 'weight': 4, 'content': [{'end': 33.337, 'text': "right now I'm going to start to talk about how we can learn about this very long standing field, of how we can specifically marry two topics.", 'start': 25.27, 'duration': 8.067}, {'end': 38.959, 'text': 'The first topic being reinforcement learning, which has existed for many, many decades,', 'start': 33.477, 'duration': 5.482}, {'end': 45.641, 'text': "together with a lot of the very recent advances in deep learning which you've already started learning about as part of this course.", 'start': 38.959, 'duration': 6.682}, {'end': 53.742, 'text': 'Now, this marriage of these two fields is actually really fascinating to me, particularly because, like I said,', 'start': 47.101, 'duration': 6.641}], 'summary': 'Marrying reinforcement learning with recent advances in deep learning.', 'duration': 28.472, 'max_score': 25.27, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/AhyznRSDjw8/pics/AhyznRSDjw825270.jpg'}, {'end': 226.842, 'src': 'embed', 'start': 70.989, 'weight': 0, 'content': [{'end': 73.892, 'text': 'right, we collect, we go out and collect that data set,', 'start': 70.989, 'duration': 2.903}, {'end': 79.497, 'text': 'we deploy it on our machine learning or deep learning algorithm and then we can evaluate on a brand new data set.', 'start': 73.892, 'duration': 5.605}, {'end': 83.001, 'text': 'but that is very different than the way things work in the real world.', 'start': 79.497, 'duration': 3.504}, {'end': 91.008, 'text': 'in the real world you have your deep learning model actually deployed together with the data together out into reality, Exploring,', 'start': 83.001, 'duration': 8.007}, {'end': 101.652, 'text': 'interacting with its environment and trying out a whole bunch of different actions and different things in that environment in order to be able to learn how to best perform any particular task that may.', 'start': 91.008, 'duration': 10.644}, {'end': 109.015, 'text': 'need to accomplish and typically we want to be able to do this without explicit human supervision right, this is the key.', 'start': 101.672, 'duration': 7.343}, {'end': 111.355, 'text': 'motivation of reinforcement learning.', 'start': 109.695, 'duration': 1.66}, {'end': 118.438, 'text': "you're going to try and learn through reinforcement, making mistakes in your world and then collecting data on those mistakes to learn how to improve.", 'start': 111.355, 'duration': 7.083}, {'end': 126.704, 'text': 'Now, this is obviously a huge field or a huge topic in the field of robotics and autonomy.', 'start': 119.758, 'duration': 6.946}, {'end': 129.386, 'text': 'You can think of self-driving cars and robot manipulation.', 'start': 126.724, 'duration': 2.662}, {'end': 134.551, 'text': "But also very recently we've started seeing incredible advances of deep reinforcement learning,", 'start': 129.786, 'duration': 4.765}, {'end': 139.815, 'text': 'specifically also on the side of gameplay and strategy making as well.', 'start': 134.551, 'duration': 5.264}, {'end': 150.671, 'text': 'So one really cool thing is that now you can even imagine this, This combination of robotics together with gameplay right?', 'start': 140.616, 'duration': 10.055}, {'end': 153.673, 'text': 'Now training robots to play against us in the real world.', 'start': 151.011, 'duration': 2.662}, {'end': 157.316, 'text': "And I'll just play this very short video on StarCraft and DeepMind.", 'start': 154.033, 'duration': 3.283}, {'end': 164.541, 'text': 'Perfect information and is played in real time.', 'start': 162.019, 'duration': 2.522}, {'end': 171.606, 'text': 'It also requires long-term planning and the ability to choose what action to take from millions and millions of possibilities.', 'start': 165.341, 'duration': 6.265}, {'end': 177.655, 'text': "I'm hoping for a 5-0, not to lose any games, but I think the realistic goal would be 4-1 in my favor.", 'start': 172.631, 'duration': 5.024}, {'end': 182.038, 'text': 'I think he looks more confident than Thielo.', 'start': 180.076, 'duration': 1.962}, {'end': 187.682, 'text': 'Thielo was quite nervous before.', 'start': 182.058, 'duration': 5.624}, {'end': 189.563, 'text': 'The room was much more tense this time.', 'start': 188.122, 'duration': 1.441}, {'end': 192.866, 'text': "I really didn't know what to expect.", 'start': 190.004, 'duration': 2.862}, {'end': 195.988, 'text': "He's been playing StarCraft pretty much since he's five.", 'start': 193.546, 'duration': 2.442}, {'end': 203.589, 'text': "I wasn't expecting the AI to be that good.", 'start': 201.368, 'duration': 2.221}, {'end': 207.351, 'text': 'Everything that he did was proper.', 'start': 205.971, 'duration': 1.38}, {'end': 209.472, 'text': 'It was calculated and it was done well.', 'start': 207.371, 'duration': 2.101}, {'end': 211.854, 'text': "I thought I'm learning something.", 'start': 210.533, 'duration': 1.321}, {'end': 216.957, 'text': "It's much better than I expected it to be.", 'start': 211.934, 'duration': 5.023}, {'end': 221.499, 'text': 'I would consider myself a good player, right? But I lost every single one of my games.', 'start': 216.997, 'duration': 4.502}, {'end': 226.842, 'text': "We're way ahead of Wai.", 'start': 225.861, 'duration': 0.981}], 'summary': 'Reinforcement learning enables robots to learn through interaction with the environment, leading to advances in robotics, autonomy, and gameplay strategies.', 'duration': 155.853, 'max_score': 70.989, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/AhyznRSDjw8/pics/AhyznRSDjw870989.jpg'}], 'start': 4.988, 'title': 'Integrating reinforcement learning and deep learning', 'summary': 'Discusses the integration of reinforcement learning and deep learning, emphasizing the shift from static datasets to real-world deployment, applications in robotics and autonomy, and the intense competition between ai and a human player in starcraft, revealing insights into their skills and performance.', 'chapters': [{'end': 91.008, 'start': 4.988, 'title': 'Reinforcement learning and deep learning marriage', 'summary': 'Discusses the integration of reinforcement learning and deep learning, highlighting the shift from static datasets to real-world deployment and exploration.', 'duration': 86.02, 'highlights': ['The integration of reinforcement learning and deep learning moves beyond static datasets, reflecting a shift towards real-world deployment and exploration.', 'The lecture explores the marriage of reinforcement learning, a long-standing field, with recent advances in deep learning, offering a unique perspective on model deployment and evaluation.']}, {'end': 164.541, 'start': 91.008, 'title': 'Reinforcement learning in robotics', 'summary': 'Discusses the motivation of reinforcement learning, emphasizing the ability to learn through making mistakes in the environment and collecting data for improvement, highlighting its applications in robotics and autonomy, including self-driving cars, robot manipulation, and deep reinforcement learning in gameplay and strategy making.', 'duration': 73.533, 'highlights': ['Reinforcement learning involves learning through making mistakes in the environment and collecting data for improvement, without explicit human supervision.', 'Applications of reinforcement learning include robotics and autonomy, such as self-driving cars, robot manipulation, and deep reinforcement learning in gameplay and strategy making.', 'Incredible advances in deep reinforcement learning have been witnessed, particularly in the field of gameplay and strategy making.', 'The combination of robotics and gameplay has emerged, with the possibility of training robots to play against humans in the real world.']}, {'end': 226.842, 'start': 165.341, 'title': 'Ai vs human in starcraft', 'summary': "Highlights the intense competition between ai and a human player in starcraft, with the human player hoping for a 4-1 victory, expressing surprise at the ai's proficiency, and acknowledging the significant gap between their skills.", 'duration': 61.501, 'highlights': ['The human player hopes for a 4-1 victory against the AI, expressing a realistic goal amidst millions of possibilities.', "The AI's proficiency surprises the human player, who acknowledges that everything the AI did was proper and well-calculated.", 'The human player acknowledges the significant skill gap between themselves and the AI, despite considering themselves a good player.']}], 'duration': 221.854, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/AhyznRSDjw8/pics/AhyznRSDjw84988.jpg', 'highlights': ['The integration of reinforcement learning and deep learning reflects a shift towards real-world deployment and exploration.', 'Applications of reinforcement learning include robotics and autonomy, such as self-driving cars and robot manipulation.', 'Incredible advances in deep reinforcement learning have been witnessed, particularly in the field of gameplay and strategy making.', 'The combination of robotics and gameplay has emerged, with the possibility of training robots to play against humans in the real world.', 'The lecture explores the marriage of reinforcement learning with recent advances in deep learning, offering a unique perspective on model deployment and evaluation.', "The AI's proficiency surprises the human player, who acknowledges the significant skill gap between themselves and the AI.", 'The human player hopes for a 4-1 victory against the AI, expressing a realistic goal amidst millions of possibilities.', 'Reinforcement learning involves learning through making mistakes in the environment and collecting data for improvement, without explicit human supervision.', 'The human player acknowledges the significant skill gap between themselves and the AI, despite considering themselves a good player.']}, {'end': 840.333, 'segs': [{'end': 276.21, 'src': 'embed', 'start': 246.41, 'weight': 7, 'content': [{'end': 252.711, 'text': "Right Up until now, we've really started focusing in the beginning part of the lectures, firstly, on what we called supervised learning.", 'start': 246.41, 'duration': 6.301}, {'end': 262.238, 'text': "Supervised learning is in this domain where we're given data in the form of Xs, our inputs, and our labels Y.", 'start': 253.771, 'duration': 8.467}, {'end': 268.484, 'text': 'And our goal here is to learn a function or a neural network that can learn to predict Y given our inputs X.', 'start': 262.238, 'duration': 6.246}, {'end': 276.21, 'text': 'So, for example, if you consider this example of an apple observing a bunch of images of apples we want to detect in the future,', 'start': 268.484, 'duration': 7.726}], 'summary': 'Focusing on supervised learning, predicting y from x data.', 'duration': 29.8, 'max_score': 246.41, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/AhyznRSDjw8/pics/AhyznRSDjw8246410.jpg'}, {'end': 380.387, 'src': 'embed', 'start': 353.478, 'weight': 0, 'content': [{'end': 357.739, 'text': "This is what the agent, let's call it, the neural network, is going to observe.", 'start': 353.478, 'duration': 4.261}, {'end': 358.62, 'text': "It's what it sees.", 'start': 357.779, 'duration': 0.841}, {'end': 365.042, 'text': 'The actions are the behaviors that this agent takes in those particular states.', 'start': 359.4, 'duration': 5.642}, {'end': 371.604, 'text': 'So the goal of reinforcement learning is to build an agent that can learn how to maximize what are called rewards.', 'start': 365.562, 'duration': 6.042}, {'end': 375.345, 'text': 'This is the third component that is specific to reinforcement learning.', 'start': 371.904, 'duration': 3.441}, {'end': 380.387, 'text': 'And you want to maximize all of those rewards over many, many time steps in the future.', 'start': 375.745, 'duration': 4.642}], 'summary': 'Reinforcement learning aims to build an agent that maximizes rewards over many time steps.', 'duration': 26.909, 'max_score': 353.478, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/AhyznRSDjw8/pics/AhyznRSDjw8353478.jpg'}, {'end': 487.119, 'src': 'embed', 'start': 460.533, 'weight': 5, 'content': [{'end': 464.014, 'text': 'So in life, for example, all of you are agents in life.', 'start': 460.533, 'duration': 3.481}, {'end': 471.256, 'text': 'The environment is the other kind of contrary approach or the contrary perspective to the agent.', 'start': 465.555, 'duration': 5.701}, {'end': 478.659, 'text': 'The environment is simply the world where that agent lives and where it operates, where it exists and it moves around in.', 'start': 471.597, 'duration': 7.062}, {'end': 484.198, 'text': 'the agent can send commands to that environment in the form of what are called actions.', 'start': 480.097, 'duration': 4.101}, {'end': 487.119, 'text': 'It can take actions in that environment.', 'start': 484.798, 'duration': 2.321}], 'summary': 'Agents interact with environment through actions in life', 'duration': 26.586, 'max_score': 460.533, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/AhyznRSDjw8/pics/AhyznRSDjw8460533.jpg'}, {'end': 557.286, 'src': 'embed', 'start': 527.105, 'weight': 4, 'content': [{'end': 531.548, 'text': 'observations is essentially how the environment responds back to the agent.', 'start': 527.105, 'duration': 4.443}, {'end': 534.05, 'text': 'right. the environment can tell the agent.', 'start': 531.548, 'duration': 2.502}, {'end': 537.813, 'text': 'you know what it should be seeing based on those actions that it just took.', 'start': 534.05, 'duration': 3.763}, {'end': 542.476, 'text': 'And it responds in the form of what is called a state.', 'start': 538.533, 'duration': 3.943}, {'end': 548.981, 'text': 'a state is simply a concrete and immediate situation that the agent finds itself in at that particular moment.', 'start': 542.476, 'duration': 6.505}, {'end': 557.286, 'text': "Now it's important to remember that, unlike other types of learning that we've covered in this course,", 'start': 550.7, 'duration': 6.586}], 'summary': 'Observations inform the agent about its current situation and guide its actions.', 'duration': 30.181, 'max_score': 527.105, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/AhyznRSDjw8/pics/AhyznRSDjw8527105.jpg'}, {'end': 651.555, 'src': 'embed', 'start': 623.283, 'weight': 3, 'content': [{'end': 629.105, 'text': 'So, for example, when we look at the total reward that an agent accumulates over the course of its lifetime,', 'start': 623.283, 'duration': 5.822}, {'end': 635.548, 'text': 'we can simply sum up all of the rewards that an agent gets after a certain time t right?', 'start': 629.105, 'duration': 6.443}, {'end': 642.051, 'text': 'So this capital R of t is the sum of all rewards from that point on into the future, into infinity.', 'start': 635.568, 'duration': 6.483}, {'end': 646.573, 'text': 'And that can be expanded to look exactly like this.', 'start': 643.732, 'duration': 2.841}, {'end': 651.555, 'text': "It's reward at time t plus the reward at time t plus 1 plus t plus 2 and so on and so forth.", 'start': 646.693, 'duration': 4.862}], 'summary': 'The total reward an agent accumulates over its lifetime is the sum of rewards from time t onwards, expanding as r(t) = reward at time t + reward at time t+1 + ...', 'duration': 28.272, 'max_score': 623.283, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/AhyznRSDjw8/pics/AhyznRSDjw8623283.jpg'}, {'end': 696.274, 'src': 'embed', 'start': 666.23, 'weight': 2, 'content': [{'end': 674.593, 'text': 'And that discounting factor is essentially multiplied by every future reward that the agent sees and is discovered by the agent.', 'start': 666.23, 'duration': 8.363}, {'end': 686.56, 'text': 'And the reason that we want to do this is actually This dampening factor is designed to make future rewards essentially worth less than rewards that we might see at this instant,', 'start': 674.693, 'duration': 11.867}, {'end': 687.581, 'text': 'at this moment, right now.', 'start': 686.56, 'duration': 1.021}, {'end': 696.274, 'text': 'Now you can think of this as basically enforcing some kind of short term a greediness in the algorithm.', 'start': 688.161, 'duration': 8.113}], 'summary': 'Discounting factor is multiplied by future rewards to make them worth less, enforcing short-term greediness.', 'duration': 30.044, 'max_score': 666.23, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/AhyznRSDjw8/pics/AhyznRSDjw8666230.jpg'}, {'end': 837.911, 'src': 'embed', 'start': 803.956, 'weight': 1, 'content': [{'end': 805.576, 'text': 'is that state at time t?', 'start': 803.956, 'duration': 1.62}, {'end': 806.216, 'text': 'A of t?', 'start': 805.576, 'duration': 0.64}, {'end': 809.157, 'text': 'is that action that you may want to take at time t,', 'start': 806.216, 'duration': 2.941}, {'end': 821, 'text': 'and the Q function of these two pieces is going to denote or capture what the expected total return would be of that agent if it took that action in that particular state.', 'start': 809.157, 'duration': 11.843}, {'end': 831.184, 'text': 'Now, one thing that I think maybe we should all be asking ourselves now is this seems like a really powerful function, right?', 'start': 822.915, 'duration': 8.269}, {'end': 837.911, 'text': 'If you had access to this type of a function, this Q function, I think you could actually perform a lot of tasks right off the bat, right?', 'start': 831.264, 'duration': 6.647}], 'summary': "Q function calculates expected return of agent's action in given state.", 'duration': 33.955, 'max_score': 803.956, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/AhyznRSDjw8/pics/AhyznRSDjw8803956.jpg'}], 'start': 228.226, 'title': 'Reinforcement learning', 'summary': 'Provides an overview of reinforcement learning, discussing its transition from supervised and unsupervised learning and introducing key components such as agents, environments, actions, observations, rewards, total rewards, discounted rewards, and the q function.', 'chapters': [{'end': 415.049, 'start': 228.226, 'title': 'Reinforcement learning overview', 'summary': 'Discusses the transition from supervised and unsupervised learning to reinforcement learning, explaining the key differences and objectives of each, with a focus on defining reinforcement learning and its components.', 'duration': 186.823, 'highlights': ['Reinforcement learning is the focus of the lecture, which involves learning algorithms based on state-action pairs and maximizing rewards over time steps.', 'Supervised learning and unsupervised learning were previously covered in the course, with supervised learning focused on learning to predict labels given inputs and unsupervised learning aimed at uncovering underlying data structure without labels.', 'Reinforcement learning aims to build an agent that learns to maximize rewards through state-action pairs, as demonstrated by the example of an agent learning to eat an apple to survive and live longer.']}, {'end': 840.333, 'start': 415.429, 'title': 'Introduction to reinforcement learning', 'summary': "Introduces the key components of reinforcement learning, including agents, environments, actions, observations, rewards, total rewards, discounted rewards, and the q function, emphasizing the significance of these elements in evaluating and maximizing an agent's performance.", 'duration': 424.904, 'highlights': ['The Q function captures the expected total return of an agent when taking a specific action in a particular state, serving as a crucial tool for maximizing rewards and optimizing agent performance.', 'Discounted rewards are calculated using a discounting factor, typically between zero and one, to prioritize immediate rewards over future ones, simulating short-term greediness in decision-making.', 'The total reward an agent accumulates over its lifetime is represented by the sum of rewards from a certain time t to infinity, and often the focus is on the discounted sum of rewards to prioritize short-term gains.', "Observations from the environment inform the agent about its state, while actions taken by the agent can result in immediate or delayed rewards, which are crucial in measuring the success of the agent's decisions.", 'The environment serves as the world where an agent operates, and actions sent by the agent can influence the environment, with the agent navigating and interacting within it.']}], 'duration': 612.107, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/AhyznRSDjw8/pics/AhyznRSDjw8228226.jpg', 'highlights': ['Reinforcement learning focuses on learning algorithms based on state-action pairs and maximizing rewards (Paragraph 1)', 'The Q function captures the expected total return of an agent when taking a specific action in a particular state (Paragraph 2)', 'Discounted rewards prioritize immediate rewards over future ones using a discounting factor (Paragraph 2)', 'The total reward an agent accumulates over its lifetime is represented by the sum of rewards from a certain time t to infinity (Paragraph 2)', 'Observations inform the agent about its state, while actions taken by the agent can result in immediate or delayed rewards (Paragraph 2)', 'The environment serves as the world where an agent operates, and actions sent by the agent can influence the environment (Paragraph 2)', 'Reinforcement learning aims to build an agent that learns to maximize rewards through state-action pairs (Paragraph 1)', 'Supervised learning focuses on learning to predict labels given inputs, while unsupervised learning uncovers underlying data structure without labels (Paragraph 1)']}, {'end': 1445.503, 'segs': [{'end': 968.452, 'src': 'embed', 'start': 934.033, 'weight': 1, 'content': [{'end': 938.096, 'text': "How can you determine what action to take? But in reality, we're not given that Q function.", 'start': 934.033, 'duration': 4.063}, {'end': 940.498, 'text': 'We have to learn that Q function using deep learning.', 'start': 938.157, 'duration': 2.341}, {'end': 947.424, 'text': "And that's what today's lecture is going to be talking about primarily is, first of all, how can we construct and learn that Q function from data?", 'start': 940.939, 'duration': 6.485}, {'end': 953.627, 'text': 'And then, of course, the final step is use that Q function to take some actions in the real world.', 'start': 948.365, 'duration': 5.262}, {'end': 960.169, 'text': "And broadly speaking, there are two classes of reinforcement learning algorithms that we're going to briefly touch on as part of today's lecture.", 'start': 953.867, 'duration': 6.302}, {'end': 963.13, 'text': "The first class is what's going to be called value learning.", 'start': 960.649, 'duration': 2.481}, {'end': 965.551, 'text': "And that's exactly this process that we've just talked about.", 'start': 963.19, 'duration': 2.361}, {'end': 968.452, 'text': 'Value learning tries to estimate our Q function.', 'start': 965.671, 'duration': 2.781}], 'summary': 'Learn q function using deep learning for action taking', 'duration': 34.419, 'max_score': 934.033, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/AhyznRSDjw8/pics/AhyznRSDjw8934033.jpg'}, {'end': 1105.101, 'src': 'embed', 'start': 1076.297, 'weight': 0, 'content': [{'end': 1080.062, 'text': 'So you want to keep hitting that ball against the top of the screen until you remove all the pixels.', 'start': 1076.297, 'duration': 3.765}, {'end': 1094.117, 'text': 'Now the Q function tells us the expected total return or the total reward that we can expect based on a given state and action pair that we may find ourselves in this game.', 'start': 1081.823, 'duration': 12.294}, {'end': 1100.199, 'text': 'Now, the first point I want to make here is that sometimes even for us as humans,', 'start': 1095.017, 'duration': 5.182}, {'end': 1105.101, 'text': 'to understand what the Q value should be is sometimes quite unintuitive, right?', 'start': 1100.199, 'duration': 4.902}], 'summary': 'Q function predicts total return based on state and action in game.', 'duration': 28.804, 'max_score': 1076.297, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/AhyznRSDjw8/pics/AhyznRSDjw81076297.jpg'}, {'end': 1391.761, 'src': 'embed', 'start': 1367.407, 'weight': 2, 'content': [{'end': 1379.547, 'text': 'The goal here is to estimate the single number output that measures what is the expected value or the expected Q value of this neural network at this particular state action pair.', 'start': 1367.407, 'duration': 12.14}, {'end': 1386.24, 'text': "Now, oftentimes, what you'll see is that if you wanted to evaluate, let's suppose, a very large action space,", 'start': 1380.319, 'duration': 5.921}, {'end': 1391.761, 'text': "it's going to be very inefficient to try the approach on the left with a very large action space,", 'start': 1386.24, 'duration': 5.521}], 'summary': 'Estimate the expected value or q value of neural network at a state-action pair with a large action space.', 'duration': 24.354, 'max_score': 1367.407, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/AhyznRSDjw8/pics/AhyznRSDjw81367407.jpg'}], 'start': 840.333, 'title': 'Q-learning, policy learning, and atari breakout', 'summary': 'Explains q function, its role in reinforcement learning, deep learning for q function, value learning, policy learning, atari breakout game, unintuitive nature of q values, neural network learning, and efficient training of q function neural network.', 'chapters': [{'end': 1035.749, 'start': 840.333, 'title': 'Q-learning and policy learning', 'summary': 'Explains the concept of q function, its role in determining the best action in reinforcement learning, and the process of learning the q function using deep learning. it also touches on the two classes of reinforcement learning algorithms: value learning and policy learning.', 'duration': 195.416, 'highlights': ['The Q function tells us the expected reward for any possible action, which is used to determine the best action given a specific state.', 'Learning the Q function involves constructing and learning it from data using deep learning.', 'Reinforcement learning algorithms are broadly classified into two classes: value learning, which estimates the Q function, and policy learning, which directly optimizes the policy for the best action.', 'The process of Q-learning involves optimizing the Q function to find the best action given a particular state, while policy learning directly optimizes the policy to determine the optimal action based on the state.']}, {'end': 1445.503, 'start': 1036.57, 'title': 'Atari breakout and q function', 'summary': 'Discusses the atari breakout game, the q function, and the unintuitive nature of q values, highlighting how the neural network learns to maximize rewards and the efficient training of the q function neural network.', 'duration': 408.933, 'highlights': ['The Q function determines the expected total return based on a given state and action pair in the Atari Breakout game, showcasing the unintuitive nature of Q values in decision-making.', 'The neural network learns to maximize rewards in the Atari Breakout game, where conservative actions result in slow progress, while more dynamic actions can lead to unexpected outcomes and significant rewards.', "Efficient training of the Q function neural network involves estimating the expected Q value for a particular state-action pair, enabling the selection of the action with the highest Q value from the neural network's output."]}], 'duration': 605.17, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/AhyznRSDjw8/pics/AhyznRSDjw8840333.jpg', 'highlights': ['The Q function determines the expected total return based on a given state and action pair in the Atari Breakout game, showcasing the unintuitive nature of Q values in decision-making.', 'Learning the Q function involves constructing and learning it from data using deep learning.', "Efficient training of the Q function neural network involves estimating the expected Q value for a particular state-action pair, enabling the selection of the action with the highest Q value from the neural network's output.", 'Reinforcement learning algorithms are broadly classified into two classes: value learning, which estimates the Q function, and policy learning, which directly optimizes the policy for the best action.']}, {'end': 1754.985, 'segs': [{'end': 1527.226, 'src': 'heatmap', 'start': 1446.084, 'weight': 0.891, 'content': [{'end': 1450.905, 'text': 'And specifically, I want all of you to think about really the best case scenario just to start with.', 'start': 1446.084, 'duration': 4.821}, {'end': 1461.067, 'text': 'How an agent would perform ideally in a particular situation or what would happen if an agent took all of the ideal actions at any given state.', 'start': 1451.065, 'duration': 10.002}, {'end': 1468.448, 'text': "This would mean that, essentially, the target return the predicted, or the value that we're trying to predict,", 'start': 1461.487, 'duration': 6.961}, {'end': 1471.328, 'text': 'the target is going to always be maximized.', 'start': 1468.448, 'duration': 2.88}, {'end': 1475.209, 'text': 'And this can serve as essentially the ground truth to the agent.', 'start': 1472.088, 'duration': 3.121}, {'end': 1478.814, 'text': 'Now, for example, to do this,', 'start': 1476.311, 'duration': 2.503}, {'end': 1488.163, 'text': "we want to formulate a loss function that's going to essentially represent our expected return if we're able to take all of the best actions right?", 'start': 1478.814, 'duration': 9.349}, {'end': 1497.112, 'text': 'So, for example, if we select an initial reward plus selecting some action in our action space that maximizes our expected return, Then,', 'start': 1488.223, 'duration': 8.889}, {'end': 1503.778, 'text': 'for the next future state, we need to apply that discounting factor and recursively apply the same equation.', 'start': 1497.112, 'duration': 6.666}, {'end': 1506.72, 'text': 'And that simply turns into our target.', 'start': 1503.938, 'duration': 2.782}, {'end': 1512.786, 'text': "Now we can ask, basically, what does our neural network predict? So that's our target.", 'start': 1506.82, 'duration': 5.966}, {'end': 1519.311, 'text': 'And we recall from previous lectures, if we have a target value, in this case our Q value is a continuous variable.', 'start': 1513.246, 'duration': 6.065}, {'end': 1527.226, 'text': 'We have also a predicted variable that is going to come as part of the output of every single one of these potential actions that could be taken.', 'start': 1519.952, 'duration': 7.274}], 'summary': 'Formulate a loss function to maximize target return for ideal agent performance.', 'duration': 81.142, 'max_score': 1446.084, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/AhyznRSDjw8/pics/AhyznRSDjw81446084.jpg'}, {'end': 1497.112, 'src': 'embed', 'start': 1468.448, 'weight': 0, 'content': [{'end': 1471.328, 'text': 'the target is going to always be maximized.', 'start': 1468.448, 'duration': 2.88}, {'end': 1475.209, 'text': 'And this can serve as essentially the ground truth to the agent.', 'start': 1472.088, 'duration': 3.121}, {'end': 1478.814, 'text': 'Now, for example, to do this,', 'start': 1476.311, 'duration': 2.503}, {'end': 1488.163, 'text': "we want to formulate a loss function that's going to essentially represent our expected return if we're able to take all of the best actions right?", 'start': 1478.814, 'duration': 9.349}, {'end': 1497.112, 'text': 'So, for example, if we select an initial reward plus selecting some action in our action space that maximizes our expected return, Then,', 'start': 1488.223, 'duration': 8.889}], 'summary': "Maximize the target to serve as the ground truth for the agent's expected return.", 'duration': 28.664, 'max_score': 1468.448, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/AhyznRSDjw8/pics/AhyznRSDjw81468448.jpg'}, {'end': 1553.015, 'src': 'embed', 'start': 1529.039, 'weight': 2, 'content': [{'end': 1536.024, 'text': "We can define what's called a Q loss, which is essentially just a very simple mean squared error loss between these two continuous variables.", 'start': 1529.039, 'duration': 6.985}, {'end': 1543.469, 'text': 'We minimize their distance over many, many different iterations of flying our neural network.', 'start': 1536.544, 'duration': 6.925}, {'end': 1553.015, 'text': 'in this environment, observing actions and observing not only the actions but, most importantly, after the action is committed or executed,', 'start': 1543.469, 'duration': 9.546}], 'summary': 'Defining q loss as mean squared error to minimize distance in neural network iterations.', 'duration': 23.976, 'max_score': 1529.039, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/AhyznRSDjw8/pics/AhyznRSDjw81529039.jpg'}, {'end': 1605.204, 'src': 'embed', 'start': 1575.652, 'weight': 3, 'content': [{'end': 1579.253, 'text': 'just to give everyone kind of a different perspective on this same problem.', 'start': 1575.652, 'duration': 3.601}, {'end': 1583.274, 'text': "So our deep neural network that we're trying to train looks like this.", 'start': 1580.034, 'duration': 3.24}, {'end': 1587.816, 'text': "It takes as input a state, it's trying to output n different numbers.", 'start': 1583.855, 'duration': 3.961}, {'end': 1592.797, 'text': 'Those n different numbers correspond to the Q value associated to n different actions.', 'start': 1588.176, 'duration': 4.621}, {'end': 1594.578, 'text': 'One Q value per action.', 'start': 1593.117, 'duration': 1.461}, {'end': 1601.274, 'text': 'Here, the actions in Atari Breakout, for example, should be three actions.', 'start': 1596.343, 'duration': 4.931}, {'end': 1605.204, 'text': 'We can either go left, we can go right, or we can do nothing, we can stay where we are.', 'start': 1601.375, 'duration': 3.829}], 'summary': 'Training a deep neural network to output n q values for different actions in atari breakout.', 'duration': 29.552, 'max_score': 1575.652, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/AhyznRSDjw8/pics/AhyznRSDjw81575652.jpg'}, {'end': 1650.47, 'src': 'embed', 'start': 1623.896, 'weight': 4, 'content': [{'end': 1629.477, 'text': 'A policy function is a function that, given a state, it determines what is the best action.', 'start': 1623.896, 'duration': 5.581}, {'end': 1631.378, 'text': "So that's different than the Q function, right?", 'start': 1629.537, 'duration': 1.841}, {'end': 1638.16, 'text': 'The Q function tells us, given a state, what is the best or what is the value, the return of every action that we could take?', 'start': 1631.398, 'duration': 6.762}, {'end': 1641.182, 'text': 'The policy function tells us one step more than that.', 'start': 1638.7, 'duration': 2.482}, {'end': 1650.47, 'text': "Given a state, what is the best action? So it's a very end-to-end way of thinking about the agent's decision-making process.", 'start': 1641.623, 'duration': 8.847}], 'summary': 'Policy function determines best action given state, a step beyond q function.', 'duration': 26.574, 'max_score': 1623.896, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/AhyznRSDjw8/pics/AhyznRSDjw81623896.jpg'}, {'end': 1736.018, 'src': 'embed', 'start': 1709.503, 'weight': 5, 'content': [{'end': 1718.567, 'text': 'now We can send this action back to the environment in the form of the game to execute the next step.', 'start': 1709.503, 'duration': 9.064}, {'end': 1727.753, 'text': "And as the agent moves through this environment, it's going to be responded with not only by new pixels that come from the game but, more importantly,", 'start': 1719.228, 'duration': 8.525}, {'end': 1728.754, 'text': 'some reward signal.', 'start': 1727.753, 'duration': 1.001}, {'end': 1736.018, 'text': "Now, it's very important to remember that the reward signals in Atari Breakout are very sparse.", 'start': 1728.854, 'duration': 7.164}], 'summary': 'Agent in atari breakout receives sparse reward signals as it interacts with the environment.', 'duration': 26.515, 'max_score': 1709.503, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/AhyznRSDjw8/pics/AhyznRSDjw81709503.jpg'}], 'start': 1446.084, 'title': 'Optimizing agent performance and deep q-learning', 'summary': 'Discusses optimizing agent performance through formulating a loss function representing the expected return, defining a q loss as mean squared error loss between continuous variables, and training the model with ground truth labels. it also explores the concept of q function and policy function in deep q-learning for atari breakout, emphasizing the process of determining the best action given a state and the sparse reward signals in the game.', 'chapters': [{'end': 1575.652, 'start': 1446.084, 'title': 'Optimizing agent performance', 'summary': 'Discusses optimizing agent performance by formulating a loss function representing the expected return, defining a q loss as mean squared error loss between continuous variables, and training the model with ground truth labels.', 'duration': 129.568, 'highlights': ['The chapter emphasizes the importance of considering the best case scenario for agent performance, aiming to maximize the target return as the ground truth to the agent.', 'The formulation of a loss function is discussed, which represents the expected return when taking all the best actions, involving recursive application of an equation to determine the target.', 'The concept of Q loss, a mean squared error loss between target and predicted Q values, is defined as a method to minimize the distance between continuous variables in the neural network training process.']}, {'end': 1754.985, 'start': 1575.652, 'title': 'Deep q-learning for atari breakout', 'summary': 'Discusses the concept of q function and policy function in deep q-learning for atari breakout, highlighting the process of determining the best action given a state and the sparse reward signals in the game.', 'duration': 179.333, 'highlights': ['The Q function in deep Q-learning outputs n different Q values corresponding to n different actions, where the optimal action is determined by maximizing these Q values.', "The policy function, derived from the Q function, determines the best action given a state, providing an end-to-end approach to the agent's decision-making process.", "Atari Breakout's sparse reward signals result in delayed rewards, often several time steps after the action is taken."]}], 'duration': 308.901, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/AhyznRSDjw8/pics/AhyznRSDjw81446084.jpg', 'highlights': ['The chapter emphasizes maximizing the target return as the ground truth for agent performance.', 'The formulation of a loss function represents the expected return when taking all the best actions.', 'The concept of Q loss minimizes the distance between continuous variables in the neural network training process.', 'The Q function in deep Q-learning outputs n different Q values corresponding to n different actions.', "The policy function determines the best action given a state, providing an end-to-end approach to the agent's decision-making process.", "Atari Breakout's sparse reward signals result in delayed rewards, often several time steps after the action is taken."]}, {'end': 2645.849, 'segs': [{'end': 1778.356, 'src': 'embed', 'start': 1756.564, 'weight': 0, 'content': [{'end': 1766.409, 'text': 'Now. one very popular or very famous approach that showed this was presented by Google DeepMind several years ago,', 'start': 1756.564, 'duration': 9.845}, {'end': 1769.231, 'text': 'where they showed that you could train a Q-value network.', 'start': 1766.409, 'duration': 2.822}, {'end': 1771.552, 'text': 'And you can see the input on the left-hand side,', 'start': 1769.271, 'duration': 2.281}, {'end': 1778.356, 'text': 'is simply the raw pixels coming from the screen all the way to the actions of a controller on the right-hand side.', 'start': 1771.552, 'duration': 6.804}], 'summary': 'Google deepmind demonstrated training a q-value network using raw pixels as input.', 'duration': 21.792, 'max_score': 1756.564, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/AhyznRSDjw8/pics/AhyznRSDjw81756564.jpg'}, {'end': 1906.547, 'src': 'embed', 'start': 1876.749, 'weight': 1, 'content': [{'end': 1882.893, 'text': 'In fact, there are now more recently, there are some solutions to achieve Q-learning and continuous action spaces.', 'start': 1876.749, 'duration': 6.144}, {'end': 1888.077, 'text': 'But for the most part, Q-learning is very well suited for discrete action spaces.', 'start': 1883.013, 'duration': 5.064}, {'end': 1892.08, 'text': "And we'll talk about ways of overcoming that with other approaches a bit later.", 'start': 1888.577, 'duration': 3.503}, {'end': 1899.077, 'text': "And the second component here is that the policy that we're learning right?", 'start': 1892.901, 'duration': 6.176}, {'end': 1906.547, 'text': "The Q function is giving rise to that policy, which is the thing that we're actually using to determine what action to take given any state.", 'start': 1899.137, 'duration': 7.41}], 'summary': 'Q-learning is well suited for discrete action spaces, but there are solutions for continuous action spaces.', 'duration': 29.798, 'max_score': 1876.749, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/AhyznRSDjw8/pics/AhyznRSDjw81876749.jpg'}, {'end': 1949.044, 'src': 'embed', 'start': 1925.51, 'weight': 4, 'content': [{'end': 1932.657, 'text': "that is very dangerous in many cases because of the fact that it's always going to pick the best value for a given state.", 'start': 1925.51, 'duration': 7.147}, {'end': 1935.078, 'text': "There's no stochasticity in that pipeline.", 'start': 1932.677, 'duration': 2.401}, {'end': 1944.983, 'text': "So you can very frequently get caught in situations where you keep repeating the same actions and you don't learn to explore potentially different options that you may be thinking of.", 'start': 1935.118, 'duration': 9.865}, {'end': 1949.044, 'text': 'So to address these very important challenges.', 'start': 1945.483, 'duration': 3.561}], 'summary': 'Highly deterministic pipeline can hinder exploration and learning of different options.', 'duration': 23.534, 'max_score': 1925.51, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/AhyznRSDjw8/pics/AhyznRSDjw81925510.jpg'}, {'end': 2000.093, 'src': 'embed', 'start': 1968.436, 'weight': 2, 'content': [{'end': 1971.598, 'text': 'Trying to infer the policy from the Q function.', 'start': 1968.436, 'duration': 3.162}, {'end': 1977.123, 'text': "we're just going to build a neural network that will directly learn that policy function from the data right.", 'start': 1971.598, 'duration': 5.525}, {'end': 1980.926, 'text': "so it kind of skips one step and we'll see how we can train those networks.", 'start': 1977.123, 'duration': 3.803}, {'end': 1989.01, 'text': 'So, before we get there, let me just revisit one more time the Q function illustration that we were looking at right?', 'start': 1983.308, 'duration': 5.702}, {'end': 1990.25, 'text': 'Q function.', 'start': 1989.13, 'duration': 1.12}, {'end': 1992.25, 'text': 'we are trying to build a neural network.', 'start': 1990.25, 'duration': 2}, {'end': 2000.093, 'text': 'outputs these Q values one value per action, and we determined the policy by looking over this state of Q values,', 'start': 1992.25, 'duration': 7.843}], 'summary': 'Building a neural network to learn the policy function directly from data, skipping a step in the process.', 'duration': 31.657, 'max_score': 1968.436, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/AhyznRSDjw8/pics/AhyznRSDjw81968436.jpg'}, {'end': 2181.697, 'src': 'embed', 'start': 2153.982, 'weight': 3, 'content': [{'end': 2160.227, 'text': 'One other very important advantage of having an output, that is, a probability distribution,', 'start': 2153.982, 'duration': 6.245}, {'end': 2165.932, 'text': 'is actually going to tie back to this other issue of Q functions and Q neural networks that we saw before.', 'start': 2160.227, 'duration': 5.705}, {'end': 2172.096, 'text': 'And that is the fact that Q functions are naturally suited towards discrete action spaces.', 'start': 2166.333, 'duration': 5.763}, {'end': 2176.756, 'text': "Now, when we're looking at this policy network, we're outputting a distribution.", 'start': 2172.376, 'duration': 4.38}, {'end': 2181.697, 'text': 'And remember, those distributions can also take continuous forms.', 'start': 2177.416, 'duration': 4.281}], 'summary': 'Outputting a probability distribution ties back to q functions and neural networks, suited towards discrete action spaces.', 'duration': 27.715, 'max_score': 2153.982, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/AhyznRSDjw8/pics/AhyznRSDjw82153982.jpg'}], 'start': 1756.564, 'title': 'Breakthroughs in q-learning and policy learning', 'summary': "Discusses google deepmind's q-value network breakthrough achieving human level performance on over half of the atari breakout games, and explores policy learning in reinforcement learning, emphasizing the advantages of policy networks and their ability to handle continuous action spaces.", 'chapters': [{'end': 1949.044, 'start': 1756.564, 'title': "Google deepmind's q-learning breakthrough", 'summary': "Discusses google deepmind's breakthrough using a q-value network to achieve human level performance on over half of the atari breakout games by training one network for different tasks, while highlighting the simplicity and downsides of q-learning.", 'duration': 192.48, 'highlights': ['Google DeepMind demonstrated surpassing human level performance on over half of Atari breakout games using a simple algorithm and a single Q-value network, with the network learning directly from the game environment without prior knowledge. (Relevance: 5)', 'Q-learning is naturally applicable to discrete action spaces, making it well-suited for such spaces, while posing challenges for continuous action spaces that require alternative solutions. (Relevance: 4)', 'The deterministic optimization of the Q function to determine the policy can lead to a lack of stochasticity, causing the model to frequently repeat the same actions and hinder exploration of different options. (Relevance: 3)']}, {'end': 2645.849, 'start': 1949.044, 'title': 'Policy learning in reinforcement learning', 'summary': 'Discusses policy learning in reinforcement learning, focusing on policy gradient algorithms and the transition from q function to policy function, highlighting the advantages of policy networks and their ability to handle continuous action spaces, using the example of training an autonomous vehicle.', 'duration': 696.805, 'highlights': ['Policy gradient algorithms directly learn the policy function from the data, skipping the step of inferring the policy from the Q function.', 'Policy networks optimize the policy function, outputting the action probability distribution, making it easier to train than Q functions and suitable for continuous action spaces.', 'Training a policy gradient neural network involves data acquisition, learning, and reinforcement for tasks like training an autonomous vehicle to follow lanes and avoid crashes.']}], 'duration': 889.285, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/AhyznRSDjw8/pics/AhyznRSDjw81756564.jpg', 'highlights': ['Google DeepMind achieved human level performance on over half of Atari breakout games using a single Q-value network. (Relevance: 5)', 'Q-learning is well-suited for discrete action spaces but poses challenges for continuous action spaces. (Relevance: 4)', 'Policy gradient algorithms directly learn the policy function from the data, skipping the step of inferring the policy from the Q function. (Relevance: 3)', 'Policy networks optimize the policy function, making it easier to train than Q functions and suitable for continuous action spaces. (Relevance: 2)', 'Deterministic optimization of the Q function can hinder exploration of different options. (Relevance: 1)']}, {'end': 3448.52, 'segs': [{'end': 2712.315, 'src': 'embed', 'start': 2670.702, 'weight': 3, 'content': [{'end': 2672.523, 'text': 'steps four and five right here?', 'start': 2670.702, 'duration': 1.821}, {'end': 2680.247, 'text': 'These are the two really important steps of how we can use those two steps to train our policy and decrease the probability of bad events,', 'start': 2672.883, 'duration': 7.364}, {'end': 2683.008, 'text': 'while promoting these likelihoods of all these good events.', 'start': 2680.247, 'duration': 2.761}, {'end': 2688.884, 'text': "So let's look at the loss function, first of all.", 'start': 2684.983, 'duration': 3.901}, {'end': 2698.567, 'text': "The loss function for a policy gradient neural network looks like this and then we'll start by dissecting it to understand why this works the way it does.", 'start': 2688.984, 'duration': 9.583}, {'end': 2701.969, 'text': 'So here we can see that the loss consists of two terms.', 'start': 2699.288, 'duration': 2.681}, {'end': 2708.871, 'text': 'The first term is this term in green, which is called the log likelihood of selecting a particular action.', 'start': 2702.049, 'duration': 6.822}, {'end': 2712.315, 'text': 'The second term is something that all of you are very familiar with already.', 'start': 2709.491, 'duration': 2.824}], 'summary': 'Training policy using steps 4 and 5 reduces bad events, promotes good events, and utilizes a loss function with two terms.', 'duration': 41.613, 'max_score': 2670.702, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/AhyznRSDjw8/pics/AhyznRSDjw82670702.jpg'}, {'end': 2789.526, 'src': 'embed', 'start': 2761.435, 'weight': 5, 'content': [{'end': 2764.756, 'text': "And you'll notice that this loss function right here.", 'start': 2761.435, 'duration': 3.321}, {'end': 2773.74, 'text': "by including this negative, we're going to minimize the likelihood of achieving any action that had low rewards in this trajectory.", 'start': 2764.756, 'duration': 8.984}, {'end': 2778.802, 'text': 'Now in our simplified example on the car example,', 'start': 2774.46, 'duration': 4.342}, {'end': 2787.125, 'text': 'all the things that had low rewards were exactly those actions that came closest to the termination part of the vehicle.', 'start': 2778.802, 'duration': 8.323}, {'end': 2789.526, 'text': 'All the things that had high rewards were the things that came in the beginning.', 'start': 2787.145, 'duration': 2.381}], 'summary': 'By using the loss function, we minimize low-reward actions, with high rewards at the beginning of the trajectory.', 'duration': 28.091, 'max_score': 2761.435, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/AhyznRSDjw8/pics/AhyznRSDjw82761435.jpg'}, {'end': 2987.644, 'src': 'embed', 'start': 2962.59, 'weight': 2, 'content': [{'end': 2970.134, 'text': "we want them to be in reality and, As part of our lab here at MIT, we've been developing this very,", 'start': 2962.59, 'duration': 7.544}, {'end': 2977.898, 'text': 'very cool brand new photorealistic simulation engine that goes beyond basically the paradigm of how simulators work today,', 'start': 2970.134, 'duration': 7.764}, {'end': 2983.982, 'text': 'which is basically defining a model of their environment and trying to synthesize that model.', 'start': 2977.898, 'duration': 6.084}, {'end': 2987.644, 'text': 'Essentially, these simulators are like glorified game engines.', 'start': 2984.362, 'duration': 3.282}], 'summary': 'Mit is developing a photorealistic simulation engine that goes beyond current simulators.', 'duration': 25.054, 'max_score': 2962.59, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/AhyznRSDjw8/pics/AhyznRSDjw82962590.jpg'}, {'end': 3146.19, 'src': 'embed', 'start': 3100.856, 'weight': 0, 'content': [{'end': 3110.879, 'text': 'And this represented actually the first time ever that reinforcement learning was used to train a policy end to end for an autonomous vehicle that could be deployed in reality.', 'start': 3100.856, 'duration': 10.023}, {'end': 3114.881, 'text': 'So that was something really cool that we created here at MIT.', 'start': 3110.919, 'duration': 3.962}, {'end': 3124.084, 'text': 'But now that we covered all of these foundations of reinforcement learning and policy learning, I want to touch on some other, maybe very exciting,', 'start': 3115.021, 'duration': 9.063}, {'end': 3126.205, 'text': "applications that we're seeing.", 'start': 3124.084, 'duration': 2.121}, {'end': 3132.247, 'text': 'And one very popular application that a lot of people will tell you about and talk about is the game of Go.', 'start': 3126.785, 'duration': 5.462}, {'end': 3138.948, 'text': 'So here reinforcement learning agents could be actually tried to put against the test against.', 'start': 3133.047, 'duration': 5.901}, {'end': 3146.19, 'text': 'you know, grandmaster level Go players and you know at the time achieved incredibly impressive results.', 'start': 3138.948, 'duration': 7.242}], 'summary': 'Mit achieved first autonomous vehicle training using reinforcement learning.', 'duration': 45.334, 'max_score': 3100.856, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/AhyznRSDjw8/pics/AhyznRSDjw83100856.jpg'}], 'start': 2646.089, 'title': 'Training neural networks with policy gradient algorithm and reinforcement learning applications', 'summary': 'Delves into the policy gradient algorithm for training neural networks, covering algorithm updates, training steps, and loss function. it also explores the challenges of deploying reinforcement learning algorithms, including the use of simulation engines, autonomous vehicle training, and mastering the game of go.', 'chapters': [{'end': 2829.098, 'start': 2646.089, 'title': 'Policy gradient neural network', 'summary': 'Discusses the policy gradient algorithm for training neural networks, focusing on updating the algorithm, steps for training the policy, and the loss function, which includes log likelihood and return terms to adjust the probabilities of actions based on rewards.', 'duration': 183.009, 'highlights': ['The loss function consists of two terms: log likelihood of selecting a particular action and the expected return at a specific time, which are used to adjust the probabilities of actions based on the obtained rewards.', 'The algorithm aims to decrease the probability of bad events while promoting the likelihoods of good events through training the policy using specific steps.', 'The car example illustrates how the algorithm adjusts the likelihood of actions based on rewards, aiming to minimize the likelihood of achieving actions with low rewards and maximize the likelihood of actions with high rewards.']}, {'end': 3448.52, 'start': 2829.818, 'title': 'Reinforcement learning applications', 'summary': 'Discusses the challenges of deploying reinforcement learning algorithms in the real world, highlighting the use of photorealistic simulation engines to bridge the sim-to-real gap, the application of reinforcement learning in training an autonomous vehicle, and the use of reinforcement learning algorithms to master the game of go.', 'duration': 618.702, 'highlights': ['The development of a photorealistic simulation engine at MIT to bridge the sim-to-real gap, allowing reinforcement learning algorithms to be transferred into reality without the gap (e.g., simulating real roads using data collected from the real world).', 'The successful deployment of reinforcement learning policies trained entirely in simulation on a full-scale autonomous vehicle, representing the first time reinforcement learning was used to train a policy end to end for an autonomous vehicle that could be deployed in reality.', 'The application of reinforcement learning algorithms, based on the same algorithms covered in the lecture, to defeat grandmaster level Go players and achieve superhuman performance, with the approach involving the use of neural networks trained to imitate human experts and bootstrap reinforcement learning algorithms to achieve superhuman performance without human supervision.']}], 'duration': 802.431, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/AhyznRSDjw8/pics/AhyznRSDjw82646089.jpg', 'highlights': ['The successful deployment of reinforcement learning policies trained entirely in simulation on a full-scale autonomous vehicle, representing the first time reinforcement learning was used to train a policy end to end for an autonomous vehicle that could be deployed in reality.', 'The application of reinforcement learning algorithms, based on the same algorithms covered in the lecture, to defeat grandmaster level Go players and achieve superhuman performance, with the approach involving the use of neural networks trained to imitate human experts and bootstrap reinforcement learning algorithms to achieve superhuman performance without human supervision.', 'The development of a photorealistic simulation engine at MIT to bridge the sim-to-real gap, allowing reinforcement learning algorithms to be transferred into reality without the gap (e.g., simulating real roads using data collected from the real world).', 'The loss function consists of two terms: log likelihood of selecting a particular action and the expected return at a specific time, which are used to adjust the probabilities of actions based on the obtained rewards.', 'The algorithm aims to decrease the probability of bad events while promoting the likelihoods of good events through training the policy using specific steps.', 'The car example illustrates how the algorithm adjusts the likelihood of actions based on rewards, aiming to minimize the likelihood of achieving actions with low rewards and maximize the likelihood of actions with high rewards.']}], 'highlights': ['The successful deployment of reinforcement learning policies trained entirely in simulation on a full-scale autonomous vehicle, representing the first time reinforcement learning was used to train a policy end to end for an autonomous vehicle that could be deployed in reality.', 'The application of reinforcement learning algorithms, based on the same algorithms covered in the lecture, to defeat grandmaster level Go players and achieve superhuman performance, with the approach involving the use of neural networks trained to imitate human experts and bootstrap reinforcement learning algorithms to achieve superhuman performance without human supervision.', 'Google DeepMind achieved human level performance on over half of Atari breakout games using a single Q-value network.', 'Reinforcement learning involves learning through making mistakes in the environment and collecting data for improvement, without explicit human supervision.', 'The Q function captures the expected total return of an agent when taking a specific action in a particular state.', 'The Q function determines the expected total return based on a given state and action pair in the Atari Breakout game, showcasing the unintuitive nature of Q values in decision-making.', 'The integration of reinforcement learning and deep learning reflects a shift towards real-world deployment and exploration.', 'The combination of robotics and gameplay has emerged, with the possibility of training robots to play against humans in the real world.', 'The lecture explores the marriage of reinforcement learning with recent advances in deep learning, offering a unique perspective on model deployment and evaluation.', "The AI's proficiency surprises the human player, who acknowledges the significant skill gap between themselves and the AI."]}