Bounded Ratio Reinforcement Learning Framework Bridges PPO Theory Gap

A novel framework called Bounded Ratio Reinforcement Learning (BRRL) has been introduced by researchers to bridge the gap between trust region techniques and the heuristic clipped objective found in Proximal Policy Optimization (PPO). This framework creates a constrained and regularized policy optimization problem, leading to an analytical optimal solution that ensures consistent performance enhancement. To accommodate parameterized policy classes, the team has devised Bounded Policy Optimization (BPO), which focuses on minimizing the advantage-weighted divergence between the policy and the optimal solution of BRRL. Additionally, they establish a lower bound for expected performance. This research is available on arXiv with the identifier 2604.18578.