【摘要】 提出一种多智能体增强式学习方法 ,每个智能体在学习过程中将其他智能体和环境区分开来 ,并且通过维持其他智能体的替代传导径迹来预测它们的行为 ,从而也确定了自身的行为 .该算法不需要知道其他智能体的 Q函数结构和奖赏函数结构 ,适用条件宽松 .仿真结果证明了所提出学习算法的有效性 ,而且相对于集中式 Q学习效率有很大的提高 .更多还原

【Abstract】 Each agent takes the others different from the environment, and maintains the others’ replacing eligibility traces to estimate the other’s behaviors during learning, at the same time estimates itself behavior. Each agent does not know the others’ structures of Q function and payoff using this multi-agent reinforcement learning without rigorous condition. The simulation results prove the validity of this proposed learning method and the high efficiency according to the centralized Q-learning.更多还原