Reinforcement learning for penalty avoiding rational policy making

Abstract

Reinforcement learning is a kind of machine learning. It aims to adapt an agent to a given environment with a clue to rewards. In general, the purpose of reinforcement learning system is to acquire an optimum policy that can maximize expected reward per an action. However, it is not always important for any environment. Especially, if we apply reinforcement learning system to engineering, environments, we expect the agent to avoid all penalties. In Markov Decision Processes, a pair of a sensory input and an action is called rule. We call a rule penalty if and only if it has a penalty or it can transit to a penalty state where it does not contribute to get any reward. After suppressing all penalty rules, we aim to make a rational policy whose expected reward per an action is larger than zero. In this paper, we propose a suppressing penalty algorithm that can suppress any penalty and get a reward constantly. By applying the algorithm to the tick-tack-toe, its effectiveness is shown.