Work In Progress: Machine Learning In Robotics

Abstract

This work investigates combining reinforcement learning and radial basis function neural network (RBFNN) learning to improve the performance of a robot that learns to perform its task. A simple task is given, and the simulator uses reinforcement learning to generate a q-learning table, which is then used to generate a neural network. Analysis is then done to determine if the combination improves the robots performance of its task