Multiagent reinforcement learning with the partly high-dimensional state space
نویسندگان
چکیده
In Multi-Agent Reinforcement Learning, each agent observe a state of other agents as a part of environment. Therefore, the state space is exponential in the number of agents and learning speed significantly decrease. Modular Q-learning [6] needs very small state space. However, the incomplete observation involves a decline in the performance. In this paper, we improve Modular Q-learning’s performance with the partly high-dimensional state space.
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ورودعنوان ژورنال:
- Systems and Computers in Japan
دوره 37 شماره
صفحات -
تاریخ انتشار 2006