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Reinforcement learning policy gradient methods analyzed for distribution mismatch

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This paper analyzes on-policy policy gradient methods in reinforcement learning, focusing on the impact of distribution mismatch. The research establishes global convergence of objective values for tabular direct and softmax parameterizations under biased updates. For tabular softmax policies, it derives exact coefficients for asymptotic objective errors, providing a criterion to assess whether distribution mismatch accelerates or slows convergence. The analysis extends to more general policy parameterizations, deriving bounds on state distribution mismatch and induced normalized policy-gradient mismatch, which diminish as the discount factor approaches one. AI

IMPACT Provides theoretical insights into the robustness of policy gradient methods, potentially informing future algorithm design.

RANK_REASON The cluster contains an academic paper detailing theoretical analysis of reinforcement learning methods. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Reinforcement learning policy gradient methods analyzed for distribution mismatch

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The cluster contains an academic paper detailing theoretical analysis of reinforcement learning methods. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Weizhen Wang, Xinzhe Zhou, Jianping He, Xiaoming Duan ·

    Analysis of On-policy Policy Gradient Methods under the Distribution Mismatch

    arXiv:2503.22244v3 Announce Type: replace Abstract: Policy gradient methods are among the most successful approaches for solving challenging reinforcement learning problems. Despite their empirical success, many state-of-the-art policy gradient algorithms for discounted problems …

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