Statistical Guarantees for Lifelong Reinforcement Learning using PAC-Bayesian Theory

• URL: http://arxiv.org/abs/2411.00401v1
• Date: Fri, 01 Nov 2024 07:01:28 GMT
• Title: Statistical Guarantees for Lifelong Reinforcement Learning using PAC-Bayesian Theory
• Authors: Zhi Zhang, Chris Chow, Yasi Zhang, Yanchao Sun, Haochen Zhang, Eric Hanchen Jiang, Han Liu, Furong Huang, Yuchen Cui, Oscar Hernan Madrid Padilla,
• Abstract summary: EPIC is a novel algorithm designed for lifelong reinforcement learning. It learns a shared policy distribution, referred to as the textitworld policy, which enables rapid adaptation to new tasks. Experiments on a variety of environments demonstrate that EPIC significantly outperforms existing methods in lifelong RL.
• Score: 37.02104729448692
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Lifelong reinforcement learning (RL) has been developed as a paradigm for extending single-task RL to more realistic, dynamic settings. In lifelong RL, the "life" of an RL agent is modeled as a stream of tasks drawn from a task distribution. We propose EPIC (\underline{E}mpirical \underline{P}AC-Bayes that \underline{I}mproves \underline{C}ontinuously), a novel algorithm designed for lifelong RL using PAC-Bayes theory. EPIC learns a shared policy distribution, referred to as the \textit{world policy}, which enables rapid adaptation to new tasks while retaining valuable knowledge from previous experiences. Our theoretical analysis establishes a relationship between the algorithm's generalization performance and the number of prior tasks preserved in memory. We also derive the sample complexity of EPIC in terms of RL regret. Extensive experiments on a variety of environments demonstrate that EPIC significantly outperforms existing methods in lifelong RL, offering both theoretical guarantees and practical efficacy through the use of the world policy.

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