English(EN) Bayesian Offline Multi-Agent Reinforcement Learning with Latent World Models

新的贝叶斯框架增强了离线多智能体强化学习

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-29 08:16

研究人员开发了一个用于离线多智能体强化学习（MARL）的贝叶斯框架，该框架解决了数据集覆盖范围有限和潜在的分布外错误带来的挑战。所提出的方法从分散的观察-动作历史中推断潜在的全局状态，并仅使用离线数据来改进后验世界模型。引入了一个新的指标，多智能体后验信息损失（MPIL），用于衡量世界模型的可靠性并指导改进和离线评估。在D4RL MuJoCo任务上的实验表明，该方法具有竞争力的策略性能和改进的世界模型预测能力。 AI

影响这项研究可能有助于在数据有限的复杂、部分可观测环境中的更鲁棒的决策。

排序理由该集群包含一篇详细介绍多智能体强化学习新框架的研究论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.MA (Multiagent) 阅读 →

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arXiv cs.MA (Multiagent) TIER_1 English(EN) · Jakob Foerster · 2026-10-29 08:16

具有潜在世界模型的贝叶斯离线多智能体强化学习

Model-based offline multi-agent reinforcement learning (MARL) provides a promising approach to multi-agent decision-making under partial observability, where the global state is unavailable from offline data. However, limited dataset coverage can lead to substantial out-of-distri…

本文如何被排名

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具有潜在世界模型的贝叶斯离线多智能体强化学习

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