In our cooperative behavior, there are two type strategies: passive behavioral strategies based on others' behavior and active behavioral strategies that oneself acts first. In order to realize a robot that can communicate with human, it is necessary to acquire such behavioral strategies. However, it is not clear how to acquire meta-strategy to switch those strategies. In this study, we have experimented with multi-agent collision avoidance simulations as an example of cooperative tasks. In the

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... experiment, we have used reinforcement learning to obtain an active and passive strategy by rewarding the interaction with agents facing each other. Additionally, we have acquired meta-strategy by reinforcement learning to selectively use those strategies and evaluated them.