A Primal-Dual-Critic Algorithm for Offline Constrained Reinforcement Learning

• URL: http://arxiv.org/abs/2306.07818v2
• Date: Thu, 19 Oct 2023 22:35:48 GMT
• Title: A Primal-Dual-Critic Algorithm for Offline Constrained Reinforcement Learning
• Authors: Kihyuk Hong, Yuhang Li, Ambuj Tewari
• Abstract summary: We propose Primal-Dual-Critic Algorithm (PDCA), a novel algorithm for offline constrained RL with general function approximation. PDCA runs a primal-dual algorithm on the Lagrangian function estimated by critics. We show that PDCA can successfully find a near saddle point of the Lagrangian, which is nearly optimal for the constrained RL problem.
• Score: 35.13329809630571
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Offline constrained reinforcement learning (RL) aims to learn a policy that maximizes the expected cumulative reward subject to constraints on expected cumulative cost using an existing dataset. In this paper, we propose Primal-Dual-Critic Algorithm (PDCA), a novel algorithm for offline constrained RL with general function approximation. PDCA runs a primal-dual algorithm on the Lagrangian function estimated by critics. The primal player employs a no-regret policy optimization oracle to maximize the Lagrangian estimate and the dual player acts greedily to minimize the Lagrangian estimate. We show that PDCA can successfully find a near saddle point of the Lagrangian, which is nearly optimal for the constrained RL problem. Unlike previous work that requires concentrability and a strong Bellman completeness assumption, PDCA only requires concentrability and realizability assumptions for sample-efficient learning.

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