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Lecture Notes in Computer Science
The existing reinforcement learning approaches have been suffering from the policy alternation of others in multiagent dynamic environments such as RoboCup competitions since other agent behaviors may cause sudden changes of state transition probabilities of which constancy is necessary for the learning to converge. A modular learning approach would be able to solve this problem if a learning agent can assign each module to one situation in which the module can regard the state transitiondoi:10.1007/978-3-540-32256-6_51 fatcat:livhqjq5nvbfdjzxckxsj5sjai