Heuristically Accelerated Reinforcement Learning methods - an overview

Abstract

The conventional reinforcement learning methods (e.g. Q-learning, SARSA) search the solution through trial and error by the properly defined reward function, therefore based on reinforcements given by the environment. The beginning of the learning phase the system does not have any knowledge about the solution, the goal of these methods are to build the knowledgebase during the learning phase. Thus the learning phase can be a long task and the number of iterations which lead to the final solution can be high. In that case the learning process can be speed up, if there are portion of knowledge about the solution and it can be injected into the learning system. The heuristically accelerated reinforcement learning methods incorporate the knowledge defined by human, due to this reason the convergence speed of the system and the number of iterations can be decreased. The main goal of the paper is to give an overview about reinforcement learning methods which heuristically accelerated and give a possibility to inject human defined knowledge into the learning system.