- To read the original paper that introduced DAgger, checkout the following paper, A Reduction of Imitation Learning and Structured Prediction to No-Regret Online Learning: https://arxiv.org/pdf/1011.0686.pdf.
- To learn more about imitation learning algorithms, checkout the following paper, Global Overview of Imitation Learning: https://arxiv.org/pdf/1801.06503.pdf.
- To learn more about inverse reinforcement learning, checkout the following survey, A Survey of Inverse Reinforcement Learning: Challenges, Methods and Progress: https://arxiv.org/pdf/1806.06877.pdf.
- 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
Further reading
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