Policy Selection Method based on Spreading Activation Model for Reinforcement Learning Agent

This paper proposes a policy selection method of a reinforcement learning agent for suitable learning in unknown or dynamic environments based on a spreading activation model in the cognitive psychology. The reinforcement learning agent saves policies learned in various environments and the agent learns flexibly by partially using suitable policy according to the environment. In the proposed method, a directed graph is created between policies, and the network is constructed by means of a policy by combining them between policies. The agent updates the network according to the environment while repeating processes of recall, activation, filtering, and learns based on the network. Agent uses this network in transfer learning. Simulation results show that reinforcement learning agent achieves task by selecting the optimal one from multiple policies by the proposed method and from the comparison of transfer learning with the proposed method and the learning efficiency of ordinary reinforcement learning, the usefulness of the proposed method.