Deterministic Policies for Constrained Reinforcement Learning in Polynomial Time

• URL: http://arxiv.org/abs/2405.14183v2
• Date: Wed, 30 Oct 2024 22:58:51 GMT
• Title: Deterministic Policies for Constrained Reinforcement Learning in Polynomial Time
• Authors: Jeremy McMahan,
• Abstract summary: Our algorithm efficiently computes near-optimal deterministic policies for constrained reinforcement learning problems. Our work answers three open questions spanning two long-standing lines of research.
• Score: 1.223779595809275
• License:
• Abstract: We present a novel algorithm that efficiently computes near-optimal deterministic policies for constrained reinforcement learning (CRL) problems. Our approach combines three key ideas: (1) value-demand augmentation, (2) action-space approximate dynamic programming, and (3) time-space rounding. Our algorithm constitutes a fully polynomial-time approximation scheme (FPTAS) for any time-space recursive (TSR) cost criteria. A TSR criteria requires the cost of a policy to be computable recursively over both time and (state) space, which includes classical expectation, almost sure, and anytime constraints. Our work answers three open questions spanning two long-standing lines of research: polynomial-time approximability is possible for 1) anytime-constrained policies, 2) almost-sure-constrained policies, and 3) deterministic expectation-constrained policies.

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