Representation Learning For Efficient Deep Multi-Agent Reinforcement Learning

• URL: http://arxiv.org/abs/2406.02890v1
• Date: Wed, 5 Jun 2024 03:11:44 GMT
• Title: Representation Learning For Efficient Deep Multi-Agent Reinforcement Learning
• Authors: Dom Huh, Prasant Mohapatra,
• Abstract summary: We present MAPO-LSO which applies a form of comprehensive representation learning devised to supplement MARL training. Specifically, MAPO-LSO proposes a multi-agent extension of transition dynamics reconstruction and self-predictive learning. Empirical results demonstrate MAPO-LSO to show notable improvements in sample efficiency and learning performance compared to its vanilla MARL counterpart.
• Score: 10.186029242664931
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Sample efficiency remains a key challenge in multi-agent reinforcement learning (MARL). A promising approach is to learn a meaningful latent representation space through auxiliary learning objectives alongside the MARL objective to aid in learning a successful control policy. In our work, we present MAPO-LSO (Multi-Agent Policy Optimization with Latent Space Optimization) which applies a form of comprehensive representation learning devised to supplement MARL training. Specifically, MAPO-LSO proposes a multi-agent extension of transition dynamics reconstruction and self-predictive learning that constructs a latent state optimization scheme that can be trivially extended to current state-of-the-art MARL algorithms. Empirical results demonstrate MAPO-LSO to show notable improvements in sample efficiency and learning performance compared to its vanilla MARL counterpart without any additional MARL hyperparameter tuning on a diverse suite of MARL tasks.

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