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首页> 外文期刊>International Journal of Knowledge-Based in Intelligent Engineering Systems >Coordination in multiagent reinforcement learning systems by virtual reinforcement signals
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Coordination in multiagent reinforcement learning systems by virtual reinforcement signals

机译：通过虚拟强化信号在多主体强化学习系统中进行协调

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摘要

This paper presents a novel method for on-line coordination in multiagent reinforcement learning systems. In this method a reinforcement-learning agent learns to select its action estimating system dynamics in terms of both the natural reward for task achievement and the virtual reward for cooperation. The virtual reward for cooperation is ascertained dynamically by a coordinating agent who estimates it from the change in degree of cooperation of all agents using a separate reinforcement learning. This technique provides adaptive coordination, requires less communication and ensures agents to be cooperative. The validity of virtual rewards for convergence in learning is verified, and the proposed method is tested on two different simulated domains to illustrate its significance. The empirical performance of the coordinated system compared to the uncoordinated system illustrates its advantages for multiagent systems.
机译：本文提出了一种在多主体强化学习系统中进行在线协调的新方法。在这种方法中，强化学习代理学习根据任务完成的自然奖励和合作的虚拟奖励来选择其动作估计系统动力学。合作的虚拟奖励是由协调代理动态确定的，协调代理使用单独的强化学习方法根据所有代理的合作程度变化来估算该奖励。该技术提供了自适应协调，需要较少的通信并确保了代理之间的协作。验证了虚拟奖励在学习中收敛的有效性，并在两个不同的模拟域上测试了该方法的有效性。与不协调系统相比，协调系统的经验性能说明了它对多主体系统的优势。

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