Enhancing the MADDPG Algorithm for Multi-Agent Learning via Action Inference and Importance Sampling

• URL: http://arxiv.org/abs/2606.05021v1
• Date: Wed, 03 Jun 2026 15:44:56 GMT
• Title: Enhancing the MADDPG Algorithm for Multi-Agent Learning via Action Inference and Importance Sampling
• Abstract summary: We introduce a novel Action Inference mechanism that enables each agent to predict other agents' intended actions.<n>Second, we apply an importance sampling strategy, using geometric distribution, in the replay buffer to prioritize more recent and informative experiences.<n>Our results indicate that Action Inference is effective in improving learning stability and inter-agent cooperation.
• Score: 3.0203351658673387
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We investigate multi-agent deep reinforcement learning and propose two enhancements to the Multi-Agent Deep Deterministic Policy Gradient (MADDPG) algorithm. First, we introduce a novel Action Inference mechanism that enables each agent to predict other agents' intended actions, thereby improving the accuracy and stability of its own policy. Second, we apply an importance sampling strategy, using geometric distribution, in the replay buffer to prioritize more recent and informative experiences, which helps mitigate the non-stationarity inherent in multi-agent environments. We evaluate both modifications on the discrete-action Predator-Prey task provided by the PettingZoo library, a flexible Python interface for general multi-agent reinforcement learning benchmarks. Our results indicate that Action Inference is effective in improving learning stability and inter-agent cooperation and that importance sampling using geometric distribution can lead to significant improvements in exploration efficiency over standard MADDPG. Code available at https://github.com/shaashwathsivakumar/MARL_Proj

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