Fast Reinforcement Learning under Uncertainties with Self-Organizing Neural Networks

Abstract

Using feedback signals from the environment, a reinforcement learning (RL) system typically discovers action policies that recommend actions effective to the states based on a Q-value function. However, uncertainties over the estimation of the Q-values can delay the convergence of RL. For fast RL convergence by accounting for such uncertainties, this paper proposes several enhancements to the estimation and learning of the Q-value using a self-organizing neural network. Specifically, a temporal difference method known as Q-learning is complemented by a Q-value Polarization procedure, which contrasts the Q-values using feedback signals on the effect of the recommended actions. The polarized Q-values are then learned by the self-organizing neural network using a Bi-directional Template Learning procedure. Furthermore, the polarized Q-values are in turn used to adapt the reward vigilance of the ART-based self-organizing neural network using a Bi-directional Adaptation procedure. The efficacy of the resultant system called Fast Learning (FL) FALCON is illustrated using two single-task problem domains with large MDPs. The experiment results from these problem domains unanimously show FL-FALCON converging faster than the compared approaches.