Oreilly – Deep Reinforcement Learning in Action, Video Edition 2020-3

• Part 1. Foundations
• Chapter 1. What is reinforcement learning?
• Chapter 1. Reinforcement learning
• Chapter 1. Dynamic programming versus Monte Carlo
• Chapter 1. The reinforcement learning framework
• Chapter 1. What can I do with reinforcement learning?
• Chapter 1. Why deep reinforcement learning?
• Chapter 1. Our didactic tool: String diagrams
• Chapter 1. What’s next?
• Chapter 1. Summary
• Chapter 2. Modeling reinforcement learning problems: Markov decision processes
• Chapter 2. Solving the multi-arm bandit
• Chapter 2. Applying bandits to optimize ad placements
• Chapter 2. Building networks with PyTorch
• Chapter 2. Solving contextual bandits
• Chapter 2. The Markov property
• Chapter 2. Predicting future rewards: Value and policy functions
• Chapter 2. Summary
• Chapter 3. Predicting the best states and actions: Deep Q-networks
• Chapter 3. Navigating with Q-learning
• Chapter 3. Preventing catastrophic forgetting: Experience replay
• Chapter 3. Improving stability with a target network
• Chapter 3. Review
• Chapter 3. Summary
• Chapter 4. Learning to pick the best policy: Policy gradient methods
• Chapter 4. Reinforcing good actions: The policy gradient algorithm
• Chapter 4. Working with OpenAI Gym
• Chapter 4. The REINFORCE algorithm
• Chapter 4. Summary
• Chapter 5. Tackling more complex problems with actor-critic methods
• Chapter 5. Distributed training
• Chapter 5. Advantage actor-critic
• Chapter 5. N-step actor-critic
• Chapter 5. Summary
• Part 2. Above and beyond
• Chapter 6. Alternative optimization methods: Evolutionary algorithms
• Chapter 6. Reinforcement learning with evolution strategies
• Chapter 6. A genetic algorithm for CartPole
• Chapter 6. Pros and cons of evolutionary algorithms
• Chapter 6. Evolutionary algorithms as a scalable alternative
• Chapter 6. Summary
• Chapter 7. Distributional DQN: Getting the full story
• Chapter 7. Probability and statistics revisited
• Chapter 7. The Bellman equation
• Chapter 7. Distributional Q-learning
• Chapter 7. Comparing probability distributions
• Chapter 7. Dist-DQN on simulated data
• Chapter 7. Using distributional Q-learning to play Freeway
• Chapter 7. Summary
• Chapter 8. Curiosity-driven exploration
• Chapter 8. Inverse dynamics prediction
• Chapter 8. Setting up Super Mario Bros.
• Chapter 8. Preprocessing and the Q-network
• Chapter 8. Setting up the Q-network and policy function
• Chapter 8. Intrinsic curiosity module
• Chapter 8. Alternative intrinsic reward mechanisms
• Chapter 8. Summary
• Chapter 9. Multi-agent reinforcement learning
• Chapter 9. Neighborhood Q-learning
• Chapter 9. The 1D Ising model
• Chapter 9. Mean field Q-learning and the 2D Ising model
• Chapter 9. Mixed cooperative-competitive games
• Chapter 9. Summary
• Chapter 10. Interpretable reinforcement learning: Attention and relational models
• Chapter 10. Relational reasoning with attention
• Chapter 10. Implementing self-attention for MNIST
• Chapter 10. Multi-head attention and relational DQN
• Chapter 10. Double Q-learning
• Chapter 10. Training and attention visualization
• Chapter 10. Summary
• Chapter 11. In conclusion: A review and roadmap
• Chapter 11. The uncharted topics in deep reinforcement learning
• Chapter 11. The end
• Appendix. Mathematics, deep learning, PyTorch
• Appendix. Calculus
• Appendix. Deep learning
• Appendix. PyTorch

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