标题： 通过时间对比学习的多智能体迁移学习 显示英文标题

标题： Multi-Agent Transfer Learning via Temporal Contrastive Learning

摘要： 本文介绍了一种用于深度多智能体强化学习的新型迁移学习框架。 该方法自动将目标条件策略与时间对比学习相结合，以发现有意义的子目标。 该方法包括预训练一个目标条件智能体，在目标领域进行微调，并使用对比学习构建一个通过子目标引导智能体的规划图。 在多智能体协作的Overcooked任务上的实验表明，相比基线方法，该方法提高了样本效率，能够解决稀疏奖励和长视野问题，并增强了可解释性。 结果突显了将目标条件策略与无监督时间抽象学习相结合在复杂多智能体迁移学习中的有效性。 与最先进的基线方法相比，我们的方法在仅需21.7%训练样本的情况下实现了相同或更好的性能。 显示英文摘要

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% of the training samples.