PPO-BR: Dual-Signal Entropy-Reward Adaptation for Trust Region Policy Optimization

• URL: http://arxiv.org/abs/2505.17714v1
• Date: Fri, 23 May 2025 10:30:58 GMT
• Title: PPO-BR: Dual-Signal Entropy-Reward Adaptation for Trust Region Policy Optimization
• Authors: Ben Rahman,
• Abstract summary: PPO-BR establishes a paradigm adaptive RL by fusing new exploration and convergence signals into a single trust region.<n>This work bridges a critical gap in phase-aware learning, enabling real-world deployment in safety-critical systems like robotic surgery.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Despite Proximal Policy Optimization (PPO) dominating policy gradient methods -- from robotic control to game AI -- its static trust region forces a brittle trade-off: aggressive clipping stifles early exploration, while late-stage updates destabilize convergence. PPO-BR establishes a new paradigm in adaptive RL by fusing exploration and convergence signals into a single bounded trust region -- a theoretically grounded innovation that outperforms five SOTA baselines with less than 2% overhead. This work bridges a critical gap in phase-aware learning, enabling real-world deployment in safety-critical systems like robotic surgery within a single adaptive mechanism. PPO-BR achieves 29.1% faster convergence by combining: (1) entropy-driven expansion (epsilon up) for exploration in high-uncertainty states, and (2) reward-guided contraction (epsilon down) for convergence stability. On six diverse benchmarks (MuJoCo, Atari, sparse-reward), PPO-BR achieves 29.1% faster convergence (p < 0.001), 2.3x lower reward variance than PPO, and less than 1.8% runtime overhead with only five lines of code change. PPO-BR's simplicity and theoretical guarantees make it ready-to-deploy in safety-critical domains -- from surgical robotics to autonomous drones. In contrast to recent methods such as Group Relative Policy Optimization (GRPO), PPO-BR offers a unified entropy-reward mechanism applicable to both language models and general reinforcement learning environments.

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