Multiple model-based reinforcement learning
Neural Computation
Reinforcement learning: a survey
Journal of Artificial Intelligence Research
Dealing with non-stationary environments using context detection
ICML '06 Proceedings of the 23rd international conference on Machine learning
Holonic multi-agent system for traffic signals control
Engineering Applications of Artificial Intelligence
Hierarchical control of traffic signals using Q-learning with tile coding
Applied Intelligence
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In this paper we propose a method for solving reinforcement learning problems in non-stationary environments. The basic idea is to create and simultaneously update multiple partial models of the environment dynamics. The learning mechanism is based on the detection of context changes, that is, on the detection of significant changes in the dynamics of the environment. Based on this motivation, we propose, formalize and show the efficiency of a method for detecting the current context and the associated model of prediction, as well as a method for updating each of the incrementally built models.