CBR for state value function approximation in reinforcement learning

Abstract

CBR is one of the techniques that can be applied to the task of approximating a function over high-dimensional, continuous spaces. In Reinforcement Learning systems a learning agent is faced with the problem of assessing the desirability of the state it finds itself in. If the state space is very large and/or continuous the availability of a suitable mechanism to approximate a value function - which estimates the value of single states - is of crucial importance. In this paper, we investigate the use of case-based methods to realise that task. The approach we take is evaluated in a case study in robotic soccer simulation.