Q-Learning: computation of optimal Q-values for evaluating the learning level in robotic tasks

摘要

A problem related to the use of reinforcement learning (RL) algorithms on real robot applications is the difficulty of measuring the learning level reached after some experience. Among the different RL algorithms, the Q-learning is the most widely used in accomplishing robotic tasks. The aim of this work is to a priori evaluate the optimal Q-values for problems where it is possible to compute the distance between the current state and the goal state of the system.