Vector Quantization Applied to Reinforcement Learning

Vector Quantization Applied to Reinforcement Learning
Proceedings of the Fifth Workshop on RoboCup. Stockholm, Sweden. August, 1999. IJCAI'99 (Postscript - 202 KB )

Abstract: Reinforcement learning has proven to be a set of successful techniques for finding optimal policies on uncertain and/or dynamic domains, such as the RoboCup. One of the problems on using such techniques appears with large state and action spaces, as it is the case of input information coming from the Robosoccer simulator. In this paper, we describe a new mechanism for solving the states generalization problem in reinforcement learning algorithms. This clustering mechanism is based on the vector quantization technique for signal analog-to-digital conversion and compression, and on the Generalized Lloyd Algorithm for the design of vector quantizers. We show some results on applying this technique to learning the interception task skill for Robosoccer agents.