Authentic Boundary Proximal Policy Optimization.

Area of Science:

• Reinforcement Learning
• Robotics
• Machine Learning

Background:

• Proximal Policy Optimization (PPO) is a widely used algorithm for complex tasks.
• Theoretical understanding of PPO's clipping mechanism and its relation to Trust Region Policy Optimization (TRPO) is limited.
• Existing PPO methods lack robust theoretical grounding for their performance improvements.

Purpose of the Study:

• To theoretically analyze PPO's clipping operation and its connection to TRPO.
• To propose novel PPO variants with improved stability and learning speed.
• To validate the effectiveness of new algorithms on continuous robotic control tasks.

Main Methods:

• Analysis of PPO's clipping effect on conservative policy iteration objective function.
• Theoretical derivation of the relationship between PPO and TRPO.
• Development of Authentic Boundary PPO (ABPPO) using an authentic boundary setting rule.
• Introduction of RMABPPO and P3DABPPO incorporating rollback clipping and penalized policy difference.

Main Results:

• Established a strict theoretical link between PPO and TRPO.
• Demonstrated that ABPPO, RMABPPO, and P3DABPPO improve learning stability.
• Showcased accelerated learning speeds in continuous robotic control tasks compared to standard PPO.
• Validated the effectiveness of novel policy optimization techniques.

Conclusions:

• The proposed ABPPO, RMABPPO, and P3DABPPO algorithms offer significant improvements over standard PPO.
• Theoretical analysis provides a foundation for understanding PPO's clipping mechanism.
• These advancements contribute to more stable and efficient reinforcement learning in robotics.