Estimation of the Change of Agents Behavior Strategy Using State-Action History

Abstract

Reinforcement learning (RL) provides a computational model to animal’s autonomous acquisition of behaviors even in an uncertain environment. Inverse reinforcement learning (IRL) is its opposite; given a history of behaviors of an agent, IRL attempts to determine the unknown characteristics, like a reward function, of the agent. Conventional IRL methods usually assume the agent has taken a stationary policy that is optimal in the environment. However, real RL agents do not necessarily take stationary policy, because they are often on the way of adapting to their own environments. Especially when facing an uncertain environment, an intelligent agent should take a mixed (or switching) strategy consisting of an exploitation that is best at the current situation and an exploration to resolve the environmental uncertainty. In this study, we propose a new IRL method that can identify both of a non-stationary policy and a fixed but unknown reward function, based on the behavioral history of a learning agent; in particular, we estimate a change point of the behavior policy from an exploratory one in the agent’s early stage of the learning and an exploitative one in its later learning stage. When applied to a computer simulation during a simple maze task of an agent, our method could identify the change point of the behavior policy and the fixed reward function, only from the agent’s history of behaviors.