Partial Action Replacement: Tackling Distribution Shift in Offline MARL

• URL: http://arxiv.org/abs/2511.07629v1
• Date: Wed, 12 Nov 2025 01:08:11 GMT
• Title: Partial Action Replacement: Tackling Distribution Shift in Offline MARL
• Authors: Yue Jin, Giovanni Montana,
• Abstract summary: offline multi-agent reinforcement learning (MARL) is severely hampered by the challenge of evaluating out-of-distribution joint actions.<n>We develop Soft-Partial Conservative Q-Learning (SPaCQL) to mitigate OOD issue and dynamically weighting different PAR strategies.<n>Our theoretical results also indicate that SPaCQL adaptively addresses distribution shift using uncertainty-informed weights.
• Score: 11.861550409939818
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Offline multi-agent reinforcement learning (MARL) is severely hampered by the challenge of evaluating out-of-distribution (OOD) joint actions. Our core finding is that when the behavior policy is factorized - a common scenario where agents act fully or partially independently during data collection - a strategy of partial action replacement (PAR) can significantly mitigate this challenge. PAR updates a single or part of agents' actions while the others remain fixed to the behavioral data, reducing distribution shift compared to full joint-action updates. Based on this insight, we develop Soft-Partial Conservative Q-Learning (SPaCQL), using PAR to mitigate OOD issue and dynamically weighting different PAR strategies based on the uncertainty of value estimation. We provide a rigorous theoretical foundation for this approach, proving that under factorized behavior policies, the induced distribution shift scales linearly with the number of deviating agents rather than exponentially with the joint-action space. This yields a provably tighter value error bound for this important class of offline MARL problems. Our theoretical results also indicate that SPaCQL adaptively addresses distribution shift using uncertainty-informed weights. Our empirical results demonstrate SPaCQL enables more effective policy learning, and manifest its remarkable superiority over baseline algorithms when the offline dataset exhibits the independence structure.

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