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Finite-Time Convergence and Sample Complexity of Actor-Critic Multi-Objective Reinforcement Learning
May 7, 2024, 4:42 a.m. | Tianchen Zhou, FNU Hairi, Haibo Yang, Jia Liu, Tian Tong, Fan Yang, Michinari Momma, Yan Gao
cs.LG updates on arXiv.org arxiv.org
Abstract: Reinforcement learning with multiple, potentially conflicting objectives is pervasive in real-world applications, while this problem remains theoretically under-explored. This paper tackles the multi-objective reinforcement learning (MORL) problem and introduces an innovative actor-critic algorithm named MOAC which finds a policy by iteratively making trade-offs among conflicting reward signals. Notably, we provide the first analysis of finite-time Pareto-stationary convergence and corresponding sample complexity in both discounted and average reward settings. Our approach has two salient features: (a) …
abstract actor actor-critic algorithm applications arxiv complexity convergence cs.lg making multi-objective multiple paper policy reinforcement reinforcement learning sample trade type while world
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