Technical Note: \cal Q-Learning
Machine Learning
TD-Gammon, a self-teaching backgammon program, achieves master-level play
Neural Computation
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Cross channel optimized marketing by reinforcement learning
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Reinforcement learning: a survey
Journal of Artificial Intelligence Research
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Reinforcement Learning (RL) has as its main objective to maximize the rewards of an objective function. This is achieved by an agent which carries out a series of actions to modify the state of the environment. The reinforcements are the cornerstone of the RL. In this work, a modification of the classic scheme of RL is proposed. Our proposal is based on applying a reinforcement with uncertainty; namely, it adds a random signal to the reinforcement. This uncertainty makes the agent incapable to learn. In this work, we consider this variation in the reinforcements as noise added to the reinforcement; therefore, the proposal is to filter the reinforcement signal in order to remove the noise. In particular, a moving average filter is used; this is one of the most simple filters used in Digital Signal Processing. The proposed variation is tested in a classical RL problem, namely, Gridworlds with different characteristics regarding size, structure and noise level. Results show that our proposed approach finds the optimal solution under some conditions in which the classical procedure cannot find it.