Efficient Sequential Policy Optimization via Off-Policy Correction in Multi-Agent Reinforcement Learning
trust region policy optimization;multi-agent learning
The paper is desk rejected for significant textual overlap with [1]. This decision was confirmed by multiple members of the program committee. Section 5 in this submission is a slight rewording of Section 2 in [1], Section 2.1 in this submission is nearly identical to section 3.1 in [1], Section 4 is nearly identical to Section 5 in [1], and the abstract is a rewording of the abstract in [1].  As such, the degree of overlapping is considered as plagiarism where the proper citation is not given when the whole texts/phrases etc are copied.

[1] Wang, X., Tian, Z., Wan, Z., Wen, Y., Wang, J., & Zhang, W. (2023). Order Matters: Agent-by-agent Policy Optimization. ICLR 2023.
Submission Desk Rejected by Program Chairs
@misc{
xu2024efficient,
title={Efficient Sequential Policy Optimization via Off-Policy Correction in Multi-Agent Reinforcement Learning},
author={Pei Xu and Junge Zhang and Kaiqi Huang},
year={2024},
url={https://openreview.net/forum?id=n6Gg0D2jWT}
}
Although trust region policy optimization methods have achieved a lot of success in cooperative multi-agent tasks, most of them face a non-stationarity problem during the learning process. Recently, sequential trust region methods that update policies agent-by-agent have shed light on alleviating the non-stationarity problem. However, these methods are still less sample-efficient when compared to their counterparts (i.e., PPO) in a single-agent setting. To narrow this efficiency gap, we propose the Off-Policyness-aware Sequential Policy Optimization (OPSPO) method, which explicitly manages the off-policyness that arises from the sequential policy update process among multiple agents. We prove that our OPSPO has the tightness of the monotonic improvement bound compared with other trust region multi-agent learning methods. Finally, we demonstrate that our OPSPO consistently outperforms strong baselines under challenging multi-agent benchmarks, including StarCraftII micromanagement tasks, Multi-agent MuJoCo, and Google Research Football full game scenarios.
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~Pei_Xu2 ~Junge_Zhang1 ~Kaiqi_Huang1
Pei Xu Junge Zhang Kaiqi Huang
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trust region policy optimization multi-agent learning
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xu|efficient_sequential_policy_optimization_via_offpolicy_correction_in_multiagent_reinforcement_learning
/pdf/3648af04f4a68a864ab70783ce4908aa4b495c31.pdf
reinforcement learning
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Efficient Sequential Policy Optimization via Off-Policy Correction in Multi-Agent Reinforcement Learning
ICLR 2025 Conference Desk Rejected Submission
ICLR.cc/2025/Conference/Desk_Rejected_Submission