M³HF: Multi-agent Reinforcement Learning from Multi-phase Human Feedback of Mixed Quality
Ziyan Wang, Zhicheng Zhang, Fei Fang, Yali Du
摘要
Designing effective reward functions in multiagent reinforcement learning (MARL) is a significant challenge, often leading to suboptimal or misaligned behaviors in complex, coordinated environments. We introduce Multi-agent Reinforcement Learning from Multi-phase Human Feedback of Mixed Quality (M 3 HF), a novel framework that integrates multi-phase human feedback of mixed quality into the MARL training process. By involving humans with diverse expertise levels to provide iterative guidance, M 3 HF leverages both expert and non-expert feedback to continuously refine agents' policies. During training, we strategically pause agent learning for human evaluation, parse feedback using large language models to assign it appropriately and update reward functions through predefined templates and adaptive weights by using weight decay and performance-based adjustments. Our approach enables the integration of nuanced human insights across various levels of quality, enhancing the interpretability and robustness of multiagent cooperation. Empirical results in challenging environments demonstrate that M 3 HF significantly outperforms state-of-the-art methods, effectively addressing the complexities of reward design in MARL and enabling broader human participation in the training process. Multi-agent Reinforcement Learning from Multi-phase Human Feedback of Mixed Quality " The r ose chef shoul d get t he t omat o f i r st , t hen t he gr een chef can t ake i t and cut i t as qui ckl y as possi bl e. The bl ue chef can t ake t he pl at e and put t he cut s on i t . " Human Feedback Rol l out Vi deo Mul t i -agent RL ... l ambda obs, act : ( 1 i f act == 5 el se 0) + ( 1 i f obs[ 9] == obs[ 0] and obs[ 10] == obs[ 1] el se 0) l ambda obs, act : ( 1 i f act == 5 el se 0) + ( 1 i f obs[ 2] == 1 el se 0) Func t i on W ei ght s Adj us t ment l ambda obs, act : ( -sqr t ( ( obs[ 19] -obs[ 0] ) * * 2 + ( obs[ 20] -obs[ 1] ) * * 2) ) Rewar d Func t i on Pool s ...
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- Training language models to follow instructions with human feedbackLong Ouyang, Jeffrey Wu, Xu Jiang, Diogo Almeida 等NeurIPS 2022 · 被引用 24,707 次
- Eureka: Human-Level Reward Design via Coding Large Language ModelsYecheng Jason Ma, William Liang, Guanzhi Wang, De-An Huang 等ICLR 2024 · 被引用 582 次
- Google Research Football: A Novel Reinforcement Learning EnvironmentKarol Kurach, Anton Raichuk, Piotr Stanczyk, Michal Zajac 等AAAI 2020 · 被引用 496 次
- PEBBLE: Feedback-Efficient Interactive Reinforcement Learning via Relabeling Experience and Unsupervised Pre-trainingKimin Lee, Laura M. Smith, Pieter AbbeelICML 2021 · 被引用 380 次
- Learning Implicit Credit Assignment for Cooperative Multi-Agent Reinforcement LearningMeng Zhou, Ziyu Liu, Pengwei Sui, Yixuan Li 等NeurIPS 2020 · 被引用 142 次
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