Decoding Global Preferences: Temporal and Cooperative Dependency Modeling in Multi-Agent Preference-Based Reinforcement Learning
Tianchen Zhu, Yue Qiu, Haoyi Zhou, Jianxin Li
Abstract
Designing accurate reward functions for reinforcement learning (RL) has long been challenging. Preference-based RL (PbRL) offers a promising approach by using human preferences to train agents, eliminating the need for manual reward design. While successful in single-agent tasks, extending PbRL to complex multi-agent scenarios is nontrivial. Existing PbRL methods lack the capacity to comprehensively capture both temporal and cooperative aspects, leading to inadequate reward functions. This work introduces an advanced multi-agent preference learning framework that effectively addresses these limitations. Based on a cascading Transformer architecture, our approach captures both temporal and cooperative dependencies, alleviating issues related to reward uniformity and intricate interactions among agents. Experimental results demonstrate substantial performance improvements in multi-agent cooperative tasks, and the reconstructed reward function closely resembles expert-defined reward functions. The source code is available at https://github.com/catezi/MAPT.
BibTeX
@article{Zhu_Qiu_Zhou_Li_2024, title={Decoding Global Preferences: Temporal and Cooperative Dependency Modeling in Multi-Agent Preference-Based Reinforcement Learning}, volume={38}, url={https://ojs.aaai.org/index.php/AAAI/article/view/29666}, DOI={10.1609/aaai.v38i15.29666}, abstractNote={Designing accurate reward functions for reinforcement learning (RL) has long been challenging. Preference-based RL (PbRL) offers a promising approach by using human preferences
to train agents, eliminating the need for manual reward design. While successful in single-agent tasks, extending PbRL to complex multi-agent scenarios is nontrivial. Existing PbRL methods lack the capacity to comprehensively capture both temporal and cooperative aspects, leading to inadequate reward functions. This work introduces an advanced multi-agent preference learning framework that effectively addresses these limitations. Based on a cascading Transformer architecture, our approach captures both temporal and cooperative dependencies, alleviating issues related to reward uniformity and intricate interactions among agents. Experimental results demonstrate substantial performance improvements in multi-agent cooperative tasks, and the reconstructed reward function closely resembles expert-defined reward functions. The source code is available at https://github.com/catezi/MAPT.}, number={15}, journal={Proceedings of the AAAI Conference on Artificial Intelligence}, author={Zhu, Tianchen and Qiu, Yue and Zhou, Haoyi and Li, Jianxin}, year={2024}, month={Mar.}, pages={17202-17210} }