Ensemble-MIX: Enhancing Sample Efficiency in Multi-Agent RL Using Ensemble Methods

• URL: http://arxiv.org/abs/2506.02841v2
• Date: Sun, 08 Jun 2025 19:44:49 GMT
• Title: Ensemble-MIX: Enhancing Sample Efficiency in Multi-Agent RL Using Ensemble Methods
• Authors: Tom Danino, Nahum Shimkin,
• Abstract summary: Multi-agent reinforcement learning (MARL) methods have achieved state-of-the-art results on a range of multi-agent tasks.<n>Yet, MARL algorithms require significantly more environment interactions than their single-agent counterparts to converge.<n>We propose a novel algorithm that combines a decomposed centralized critic with decentralized ensemble learning.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Multi-agent reinforcement learning (MARL) methods have achieved state-of-the-art results on a range of multi-agent tasks. Yet, MARL algorithms typically require significantly more environment interactions than their single-agent counterparts to converge, a problem exacerbated by the difficulty in exploring over a large joint action space and the high variance intrinsic to MARL environments. To tackle these issues, we propose a novel algorithm that combines a decomposed centralized critic with decentralized ensemble learning, incorporating several key contributions. The main component in our scheme is a selective exploration method that leverages ensemble kurtosis. We extend the global decomposed critic with a diversity-regularized ensemble of individual critics and utilize its excess kurtosis to guide exploration toward high-uncertainty states and actions. To improve sample efficiency, we train the centralized critic with a novel truncated variation of the TD($\lambda$) algorithm, enabling efficient off-policy learning with reduced variance. On the actor side, our suggested algorithm adapts the mixed samples approach to MARL, mixing on-policy and off-policy loss functions for training the actors. This approach balances between stability and efficiency and outperforms purely off-policy learning. The evaluation shows our method outperforms state-of-the-art baselines on standard MARL benchmarks, including a variety of SMAC II maps.

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