复杂领域中扩展多智能体强化学习

D. Xiao, A. Tan
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引用次数: 8

摘要

TD- falcon(用于学习、认知和导航的时间差异融合架构)是一类自组织神经网络,它结合了用于实时强化学习的时间差异(TD)方法。为了进一步提高TD-FALCON在复杂多智能体领域的学习效率,本文提出了策略共享和邻近智能体机制两种策略。通过在交通控制问题域和羊群任务上的实验,我们证明了这些策略使TD-FALCON在复杂的多智能体域保持功能和适应性。
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Scaling Up Multi-agent Reinforcement Learning in Complex Domains
TD-FALCON (temporal difference-fusion architecture for learning, cognition, and navigation) is a class of self-organizing neural networks that incorporates temporal difference (TD) methods for real-time reinforcement learning. In this paper, we present two strategies, i.e. policy sharing and neighboring-agent mechanism, to further improve the learning efficiency of TD-FALCON in complex multi-agent domains. Through experiments on a traffic control problem domain and the herding task, we demonstrate that those strategies enable TD-FALCON to remain functional and adaptable in complex multi-agent domains.
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