This section is an introduction to reinforcement learning. It includes building the theoretical foundation and setting up the environment that is needed in the upcoming chapters.
This section includes the following chapters:
Section 1: Algorithms and Environments
by Andrea Lonza
Reinforcement Learning Algorithms with Python
- Title Page
- Copyright and Credits
- Dedication
- About Packt
- Contributors
- Preface
- Section 1: Algorithms and Environments
- The Landscape of Reinforcement Learning
- Implementing RL Cycle and OpenAI Gym
- Solving Problems with Dynamic Programming
- Section 2: Model-Free RL Algorithms
- Q-Learning and SARSA Applications
- Deep Q-Network
- Learning Stochastic and PG Optimization
- TRPO and PPO Implementation
- DDPG and TD3 Applications
- Section 3: Beyond Model-Free Algorithms and Improvements
- Model-Based RL
- Imitation Learning with the DAgger Algorithm
- Understanding Black-Box Optimization Algorithms
- Developing the ESBAS Algorithm
- Practical Implementation for Resolving RL Challenges
- Assessments
- Other Books You May Enjoy
Section 1: Algorithms and Environments
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