Multi-Agent Transfer Learning via Temporal Contrastive Learning

• URL: http://arxiv.org/abs/2406.01377v1
• Date: Mon, 3 Jun 2024 14:42:14 GMT
• Title: Multi-Agent Transfer Learning via Temporal Contrastive Learning
• Authors: Weihao Zeng, Joseph Campbell, Simon Stepputtis, Katia Sycara,
• Abstract summary: This paper introduces a novel transfer learning framework for deep multi-agent reinforcement learning. The approach automatically combines goal-conditioned policies with temporal contrastive learning to discover meaningful sub-goals.
• Score: 8.487274986507922
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This paper introduces a novel transfer learning framework for deep multi-agent reinforcement learning. The approach automatically combines goal-conditioned policies with temporal contrastive learning to discover meaningful sub-goals. The approach involves pre-training a goal-conditioned agent, finetuning it on the target domain, and using contrastive learning to construct a planning graph that guides the agent via sub-goals. Experiments on multi-agent coordination Overcooked tasks demonstrate improved sample efficiency, the ability to solve sparse-reward and long-horizon problems, and enhanced interpretability compared to baselines. The results highlight the effectiveness of integrating goal-conditioned policies with unsupervised temporal abstraction learning for complex multi-agent transfer learning. Compared to state-of-the-art baselines, our method achieves the same or better performances while requiring only 21.7% of the training samples.

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