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This paper presents ToPPO, a novel off-policy extension to Proximal Policy Optimization (PPO) that theoretically justifies and computationally optimizes the use of disparate data sources for reinforcement learning. By establishing a new policy improvement lower bound, the method ensures monotonic performance gains while addressing PPO's traditional limitations in leveraging existing datasets. The approach offers significant potential for improving sample efficiency in complex RL tasks without compromising training stability.
Transductive Off-policy Proximal Policy Optimization
arXiv:2406.03894v2 Announce Type: replace Abstract: Proximal Policy Optimization (PPO) is a popular model-free reinforcement learning algorithm, esteemed for its simplicity and efficacy. However, due to its inherent on-policy nature, its proficiency in harnessing data from disparate policies is constrained. This paper introduces a novel off-policy extension to the original PPO method, christened Transductive Off-policy PPO (ToPPO). Herein, we provide theoretical justification for incorporating off-policy data in PPO training and prudent guidelines for its safe application. Our contribution includes a novel formulation of the policy improvement lower bound for prospective policies derived from off-policy data, accompanied by a computationally efficient mechanism to optimize this bound, underpinned by assurances of monotonic improvement. Comprehensive experimental results across six representative tasks underscore ToPPO's promising performance.
Abstract:Proximal Policy Optimization (PPO) is a popular model-free reinforcement learning algorithm, esteemed for its simplicity and efficacy. However, due to its inherent on-policy nature, its proficiency in harnessing data from disparate policies is constrained. This paper introduces a novel off-policy extension to the original PPO method, christened Transductive Off-policy PPO (ToPPO). Herein, we provide theoretical justification for incorporating off-policy data in PPO training and prudent guidelines for its safe application. Our contribution includes a novel formulation of the policy improvement lower bound for prospective policies derived from off-policy data, accompanied by a computationally efficient mechanism to optimize this bound, underpinned by assurances of monotonic improvement. Comprehensive experimental results across six representative tasks underscore ToPPO's promising performance.
Machine Learning (cs.LG)
arXiv:2406.03894 [cs.LG]
(or arXiv:2406.03894v2 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2406.03894 Focus to learn more arXiv-issued DOI via DataCite
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