Rapid behavior learning in multi-agent environment based on state value estimation of others

The existing reinforcement learning approaches have been suffering from the curse of dimension problem when they are applied to multiagent dynamic environments. One of the typical examples is a case of RoboCup competitions since other agents and their behaviors easily cause state and action space explosion. This paper presents a method of modular learning in a multiagent environment by which the learning agent can acquire cooperative behaviors with its team mates and competitive ones against its opponents. The key ideas to resolve the issue are as follows. First, a two-layer hierarchical system with multi learning modules is adopted to reduce the size of the sensor and action spaces. The state space of the top layer consists of the state values from the lower level, and the macro actions are used to reduce the size of the physical action space. Second, the state of the other to what extent it is close to its own goal is estimated by observation and used as a state value in the top layer state space to realize the cooperative/competitive behaviors. The method is applied to 4 (defense team) on 5 (offense team) game task, and the learning agent successfully acquired the teamwork plays (pass and shoot) within much shorter learning time (30 times quicker than the earlier work).