Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Metalearning and neuromodulation
Neural Networks - Computational models of neuromodulation
Reinforcement learning: a survey
Journal of Artificial Intelligence Research
On the complexity of solving Markov decision problems
UAI'95 Proceedings of the Eleventh conference on Uncertainty in artificial intelligence
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Reinforcement Learning is an optimal adaptive optimization method for stationary environments. For non-stationary environments where the transition function and reward structure change over time, the traditional algorithms seems to be ineffective in order to follow the environmental changes. In this paper we propose the Anomaly Detection Q-learning algorithm which increase learning abilities of standard Q-learning algorithm by applying Chauvenet's criterion to detects anomalies.