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New Bayesian framework enhances offline multi-agent reinforcement learning

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a Bayesian framework for offline multi-agent reinforcement learning (MARL) that addresses challenges posed by limited dataset coverage and potential out-of-distribution errors. The proposed method infers latent global states from decentralized observation-action histories and refines a posterior world model using only offline data. A novel metric, multi-agent posterior information loss (MPIL), is introduced to measure world model reliability and guide refinement and offline evaluation. Experiments on D4RL MuJoCo tasks show competitive policy performance and improved world model prediction. AI

IMPACT This research could lead to more robust decision-making in complex, partially observable environments with limited data.

RANK_REASON The cluster contains a research paper detailing a new framework for multi-agent reinforcement learning. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.MA (Multiagent) →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New Bayesian framework enhances offline multi-agent reinforcement learning

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The cluster contains a research paper detailing a new framework for multi-agent reinforcement learning. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.MA (Multiagent) TIER_1 English(EN) · Jakob Foerster ·

    Bayesian Offline Multi-Agent Reinforcement Learning with Latent World Models

    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…

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