- Would you use a model-based or a model-free algorithm if you had only 10 games in which to train your agent to play checkers?
- What are the disadvantages of model-based algorithms?
- If a model of the environment is unknown, how can it be learned?
- Why are data aggregation methods used?
- How does ME-TRPO stabilize training?
- How does using an ensemble of models improve policy learning?
- 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
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