CBR for State Value Function Approximation in Reinforcement Learning

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.

• Type: Conference
• Year: 2005
• Where: ICCBR
• Authors: Thomas Gabel, Martin A. Riedmiller
• Keywords: Continuous Spaces, ICCBR 2005, Reinforcement Learning, Robotic Soccer Simulation