- What is RL?
- What is the end goal of an agent?
- What are the main differences between supervised learning and RL?
- What are the benefits of combining deep learning and RL?
- Where does the term "reinforcement" come from?
- What is the difference between policy and value functions?
- Can the model of an environment be learned through interacting with it?
- 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
Questions
-
No Comment
..................Content has been hidden....................
You can't read the all page of ebook, please click here login for view all page.