Deep Reinforcement Learning in Action, Video Edition
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11:59:36
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Appendix. Calculus Deep Reinforcement Learning in Action, Video Edition.mp4
10:56
Appendix. Deep learning Deep Reinforcement Learning in Action, Video Edition.mp4
01:57
Appendix. Mathematics, deep learning, PyTorch Deep Reinforcement Learning in Action, Video Edition.mp4
07:06
Appendix. PyTorch Deep Reinforcement Learning in Action, Video Edition.mp4
04:43
Chapter 1. Dynamic programming versus Monte Carlo Deep Reinforcement Learning in Action, Video Edition.mp4
05:41
Chapter 1. Our didactic tool String diagrams Deep Reinforcement Learning in Action, Video Edition.mp4
05:00
Chapter 1. Reinforcement learning Deep Reinforcement Learning in Action, Video Edition.mp4
06:55
Chapter 1. Summary Deep Reinforcement Learning in Action, Video Edition.mp4
02:24
Chapter 1. The reinforcement learning framework Deep Reinforcement Learning in Action, Video Edition.mp4
09:36
Chapter 1. Whats next Deep Reinforcement Learning in Action, Video Edition.mp4
02:31
Chapter 1. What can I do with reinforcement learning Deep Reinforcement Learning in Action, Video Edition.mp4
06:13
Chapter 1. What is reinforcement learning Deep Reinforcement Learning in Action, Video Edition.mp4
05:11
Chapter 1. Why deep reinforcement learning Deep Reinforcement Learning in Action, Video Edition.mp4
05:24
Chapter 2. Applying bandits to optimize ad placements Deep Reinforcement Learning in Action, Video Edition.mp4
06:06
Chapter 2. Building networks with PyTorch Deep Reinforcement Learning in Action, Video Edition.mp4
04:40
Chapter 2. Modeling reinforcement learning problems Markov decision processes Deep Reinforcement Learning in Action, Video Edition.mp4
11:17
Chapter 2. Predicting future rewards Value and policy functions Deep Reinforcement Learning in Action, Video Edition.mp4
07:44
Chapter 2. Solving contextual bandits Deep Reinforcement Learning in Action, Video Edition.mp4
09:35
Chapter 2. Solving the multi-arm bandit Deep Reinforcement Learning in Action, Video Edition.mp4
20:19
Chapter 2. Summary Deep Reinforcement Learning in Action, Video Edition.mp4
03:04
Chapter 2. The Markov property Deep Reinforcement Learning in Action, Video Edition.mp4
06:05
Chapter 3. Improving stability with a target network Deep Reinforcement Learning in Action, Video Edition.mp4
10:21
Chapter 3. Navigating with Q-learning Deep Reinforcement Learning in Action, Video Edition.mp4
34:04
Chapter 3. Predicting the best states and actions Deep Q-networks Deep Reinforcement Learning in Action, Video Edition.mp4
05:24
Chapter 3. Preventing catastrophic forgetting Experience replay Deep Reinforcement Learning in Action, Video Edition.mp4
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Chapter 3. Review Deep Reinforcement Learning in Action, Video Edition.mp4
06:40
Chapter 3. Summary Deep Reinforcement Learning in Action, Video Edition.mp4
02:19
Chapter 4. Learning to pick the best policy Policy gradient methods Deep Reinforcement Learning in Action, Video Edition.mp4
15:05
Chapter 4. Reinforcing good actions The policy gradient algorithm Deep Reinforcement Learning in Action, Video Edition.mp4
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Chapter 4. Summary Deep Reinforcement Learning in Action, Video Edition.mp4
02:15
Chapter 4. The REINFORCE algorithm Deep Reinforcement Learning in Action, Video Edition.mp4
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Chapter 4. Working with OpenAI Gym Deep Reinforcement Learning in Action, Video Edition.mp4
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Chapter 5. Advantage actor-critic Deep Reinforcement Learning in Action, Video Edition.mp4
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Chapter 5. Distributed training Deep Reinforcement Learning in Action, Video Edition.mp4
11:29
Chapter 5. N-step actor-critic Deep Reinforcement Learning in Action, Video Edition.mp4
09:44
Chapter 5. Summary Deep Reinforcement Learning in Action, Video Edition.mp4
01:49
Chapter 5. Tackling more complex problems with actor-critic methods Deep Reinforcement Learning in Action, Video Edition.mp4
18:06
Chapter 6. Alternative optimization methods Evolutionary algorithms Deep Reinforcement Learning in Action, Video Edition.mp4
05:10
Chapter 6. A genetic algorithm for CartPole Deep Reinforcement Learning in Action, Video Edition.mp4
09:34
Chapter 6. Evolutionary algorithms as a scalable alternative Deep Reinforcement Learning in Action, Video Edition.mp4
13:33
Chapter 6. Pros and cons of evolutionary algorithms Deep Reinforcement Learning in Action, Video Edition.mp4
06:13
Chapter 6. Reinforcement learning with evolution strategies Deep Reinforcement Learning in Action, Video Edition.mp4
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Chapter 6. Summary Deep Reinforcement Learning in Action, Video Edition.mp4
01:02
Chapter 7. Comparing probability distributions Deep Reinforcement Learning in Action, Video Edition.mp4
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Chapter 7. Distributional DQN Getting the full story Deep Reinforcement Learning in Action, Video Edition.mp4
12:22
Chapter 7. Distributional Q-learning Deep Reinforcement Learning in Action, Video Edition.mp4
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Chapter 7. Dist-DQN on simulated data Deep Reinforcement Learning in Action, Video Edition.mp4
07:07
Chapter 7. Probability and statistics revisited Deep Reinforcement Learning in Action, Video Edition.mp4
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Chapter 7. Summary Deep Reinforcement Learning in Action, Video Edition.mp4
01:15
Chapter 7. The Bellman equation Deep Reinforcement Learning in Action, Video Edition.mp4
04:34
Chapter 7. Using distributional Q-learning to play Freeway Deep Reinforcement Learning in Action, Video Edition.mp4
09:48
Chapter 8. Alternative intrinsic reward mechanisms Deep Reinforcement Learning in Action, Video Edition.mp4
06:49
Chapter 8. Curiosity-driven exploration Deep Reinforcement Learning in Action, Video Edition.mp4
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Chapter 8. Intrinsic curiosity module Deep Reinforcement Learning in Action, Video Edition.mp4
17:50
Chapter 8. Inverse dynamics prediction Deep Reinforcement Learning in Action, Video Edition.mp4
07:17
Chapter 8. Preprocessing and the Q-network Deep Reinforcement Learning in Action, Video Edition.mp4
03:51
Chapter 8. Setting up Super Mario Bros. Deep Reinforcement Learning in Action, Video Edition.mp4
04:17
Chapter 8. Setting up the Q-network and policy function Deep Reinforcement Learning in Action, Video Edition.mp4
07:12
Chapter 8. Summary Deep Reinforcement Learning in Action, Video Edition.mp4
01:22
Chapter 9. Mean field Q-learning and the 2D Ising model Deep Reinforcement Learning in Action, Video Edition.mp4
18:39
Chapter 9. Mixed cooperative-competitive games Deep Reinforcement Learning in Action, Video Edition.mp4
16:46
Chapter 9. Multi-agent reinforcement learning Deep Reinforcement Learning in Action, Video Edition.mp4
11:43
Chapter 9. Neighborhood Q-learning Deep Reinforcement Learning in Action, Video Edition.mp4
07:49
Chapter 9. Summary Deep Reinforcement Learning in Action, Video Edition.mp4
01:01
Chapter 9. The 1D Ising model Deep Reinforcement Learning in Action, Video Edition.mp4
19:42
Chapter 10. Double Q-learning Deep Reinforcement Learning in Action, Video Edition.mp4
03:59
Chapter 10. Implementing self-attention for MNIST Deep Reinforcement Learning in Action, Video Edition.mp4
24:31
Chapter 10. Interpretable reinforcement learning Attention and relational models Deep Reinforcement Learning in Action, Video Edition.mp4
11:18
Chapter 10. Multi-head attention and relational DQN Deep Reinforcement Learning in Action, Video Edition.mp4
15:32
Chapter 10. Relational reasoning with attention Deep Reinforcement Learning in Action, Video Edition.mp4
29:30
Chapter 10. Summary Deep Reinforcement Learning in Action, Video Edition.mp4
03:15
Chapter 10. Training and attention visualization Deep Reinforcement Learning in Action, Video Edition.mp4
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Chapter 11. In conclusion A review and roadmap Deep Reinforcement Learning in Action, Video Edition.mp4
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Chapter 11. The end Deep Reinforcement Learning in Action, Video Edition.mp4
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Chapter 11. The uncharted topics in deep reinforcement learning Deep Reinforcement Learning in Action, Video Edition.mp4
12:45
Part 1. Foundations Deep Reinforcement Learning in Action, Video Edition.mp4
01:02
Part 2. Above and beyond Deep Reinforcement Learning in Action, Video Edition.mp4
01:35
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- language english
- Training sessions 77
- duration 11:59:36
- Release Date 2024/02/10
