This work advances offline multi-agent reinforcement learning by improving the stability of value decom-position and multi-agent policy extraction, and contributes to the broader field of machine. Despite remarkable achievements in single-agent offline reinforcement learning (RL), multi-agent RL (MARL) has struggled to adopt this paradigm, largely persisting with on-policy training and self-play. Sep 21, 2023 · In this work, we present OMIGA, a new offline multi-agent RL algorithm with implicit global-to-local value regularization.
Apr 11, 2025 · To address these issues, we propose a new offline MARL algorithm, named In-Sample Sequential Policy Optimization (InSPO). InSPO sequentially updates each agent's policy in an in . Dec 10, 2023 · Most existing offline MARL studies simply apply offline data-related regularizations on individual agents, without fully considering the multi-agent system at the global level. In this work, . This work advances offline multi-agent reinforcement learning (MARL) by addressing the challenge of unbiased decomposition of multimodal joint action behavior distributions.
Dec 10, 2024 · To address these issues, we propose a new offline MARL algorithm, named In-Sample Sequential Policy Optimization (InSPO). InSPO sequentially updates each agent’s policy in an in . Feb 16, 2025 · Our algorithm, SCAlable Multi-agent FQI (SCAM-FQI), is then evaluated on a distributed decision-making problem. The empirical results align with our theoretical findings, . Offline reinforcement learning (RL) has garnered considerable attention in recent years due to its attractive capability of learning policies from offline datas.
A Recipe for Stable Offline Multi-agent Reinforcement Learning.
Offline Multi-Agent Reinforcement Learning with Implicit.
Offline Multi-Agent Reinforcement Learning via In-Sample Sequential.
- Offline Multi-agent Reinforcement Learning via Score Decomposition.
- Scalable Multi-Agent Offline Reinforcement Learning and the Role of.
- Offline Multi-Agent Reinforcement Learning in Custom Game Scenario.
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