Finite-Time Convergence and Sample Complexity of Actor-Critic Multi-Objective Reinforcement Learning

no code implementations • 5 May 2024 • Tianchen Zhou, FNU Hairi, Haibo Yang, Jia Liu, Tian Tong, Fan Yang, Michinari Momma, Yan Gao

Reinforcement learning with multiple, potentially conflicting objectives is pervasive in real-world applications, while this problem remains theoretically under-explored.

Multi-Objective Reinforcement Learning reinforcement-learning

Sample and Communication Efficient Fully Decentralized MARL Policy Evaluation via a New Approach: Local TD update

no code implementations • 23 Mar 2024 • FNU Hairi, Zifan Zhang, Jia Liu

This leads to an interesting open question: Can the local TD-update approach entail low sample and communication complexities?

Multi-agent Reinforcement Learning

Finite-Time Convergence and Sample Complexity of Multi-Agent Actor-Critic Reinforcement Learning with Average Reward

no code implementations • ICLR 2022 • FNU Hairi, Jia Liu, Songtao Lu

In this paper, we establish the first finite-time convergence result of the actor-critic algorithm for fully decentralized multi-agent reinforcement learning (MARL) problems with average reward.

Multi-agent Reinforcement Learning reinforcement-learning +1