Summary
This paper introduces Bounded Ratio Reinforcement Learning (BRRL) to address a theoretical disconnect in Proximal Policy Optimization (PPO), a widely used on-policy reinforcement learning algorithm. While PPO is known for its scalability and robustness, its heuristic clipped objective lacks a strong foundation in trust region methods. BRRL bridges this gap by formulating a novel regularized and constrained policy optimization framework, aiming to provide a more theoretically sound basis for PPO-like algorithms.
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[Paper] Ruka-v2: Tendon Driven Open-Source Dexterous Hand with Wrist and Abduction for Robot Learning
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