Multi-Agent Transfer Learning via Temporal Contrastive Learning
<|reference_start|>Multi-Agent Transfer Learning via Temporal Contrastive Learning: 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.<|reference_end|>
@article{zeng2024multi-agent,
    title={Multi-Agent Transfer Learning via Temporal Contrastive Learning},
    author={Weihao Zeng, Joseph Campbell, Simon Stepputtis, Katia Sycara},
    journal={2024 IEEE International Conference on Robotics and Automation
  (ICRA) 2024},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.01377},
    primaryClass={cs.AI}
}