Kazuhiro Edazawa

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The conventional reinforcement learning approaches have difficulties in handling the policy alternation of the opponents because it may cause dynamic changes of state transition probabilities of which stability is necessary for the learning to converge. A multiple learning module approach would provide one solution for this problem. If we can assign(More)
The conventional reinforcement learning approaches have difficulties to handle the policy alternation of the opponents because it may cause dynamic changes of state transition probabilities of which stability is necessary for the learning to converge. This paper presents a method of multi-module reinforcement learning in a multiagent environment , by which(More)
Existing reinforcement learning approaches have been suffering from the policy alternation of others in multiagent dynamic environments. A typical example is the case of RoboCup competitions because other agent behaviors may cause sudden changes in state transition probabilities in which constancy is needed for the learning to converge. The keys for(More)
The existing reinforcement learning approaches have been suffering from the policy alternation of others in multiagent dynamic environments. A typical example is a case of RoboCup competitions since other agent behaviors may cause sudden changes in state transition probabilities of which constancy is needed for the learning to converge. The keys for(More)
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(More)
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