Multi-Agent Transfer Learning via Temporal Contrastive Learning
CoRR(2024)
摘要
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
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