更新时间：2021-06-25 20:47:21
封面
版权信息
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Preface
What this book covers
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Chapter 1. What is Reinforcement Learning?
Learning – supervised unsupervised and reinforcement
RL formalisms and relations
Markov decision processes
Summary
Chapter 2. OpenAI Gym
The anatomy of the agent
Hardware and software requirements
OpenAI Gym API
The random CartPole agent
The extra Gym functionality – wrappers and monitors
Chapter 3. Deep Learning with PyTorch
Tensors
Gradients
NN building blocks
Custom layers
Final glue – loss functions and optimizers
Monitoring with TensorBoard
Example – GAN on Atari images
Chapter 4. The Cross-Entropy Method
Taxonomy of RL methods
Practical cross-entropy
Cross-entropy on CartPole
Cross-entropy on FrozenLake
Theoretical background of the cross-entropy method
Chapter 5. Tabular Learning and the Bellman Equation
Value state and optimality
The Bellman equation of optimality
Value of action
The value iteration method
Value iteration in practice
Q-learning for FrozenLake
Chapter 6. Deep Q-Networks
Real-life value iteration
Tabular Q-learning
Deep Q-learning
DQN on Pong
Chapter 7. DQN Extensions
The PyTorch Agent Net library
Basic DQN
N-step DQN
Double DQN
Noisy networks
Prioritized replay buffer
Dueling DQN
Categorical DQN
Combining everything
References
Chapter 8. Stocks Trading Using RL
Trading
Data
Problem statements and key decisions
The trading environment
Models
Training code
Results
Things to try
Chapter 9. Policy Gradients – An Alternative
Values and policy
The REINFORCE method
REINFORCE issues
PG on CartPole
PG on Pong
Chapter 10. The Actor-Critic Method
Variance reduction
CartPole variance
Actor-critic
A2C on Pong
A2C on Pong results
Tuning hyperparameters
Chapter 11. Asynchronous Advantage Actor-Critic
Correlation and sample efficiency
Adding an extra A to A2C
Multiprocessing in Python
A3C – data parallelism
A3C – gradients parallelism
Chapter 12. Chatbots Training with RL
Chatbots overview
