Multi-Faceted Decision Making Using Multiple Reinforcement Learning to Reducing Wasteful Actions

Reinforcement learning can lead to autonomous behavior depending on the environment. However, in complex and high-dimensional environments, such as real environments, a large number of trials are required for learning. In this paper, we propose a solution for the learning problem using local learning to select an action based on the surrounding environmental information. Simulation experiments were conducted using maze problems, pitfall problems, and environments with random agents. The actions that did not contribute to task accomplishment were compared between the proposed method and ordinary reinforcement learning method.