A work by Schulman and others shows that this is possible. Indeed, it uses a similar idea to TRPO while reducing the complexity of the method. This method is called Proximal Policy Optimization (PPO) and its strength is in the use of the first-order optimization only, without degrading the reliability compared to TRPO. PPO is also more general and sample-efficient than TRPO and enables multi updates with mini-batches.
- 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
Proximal Policy Optimization
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