RL-CD: Dealing with Non-Stationarity in Reinforcement Learning

Bruno C. da Silva, Eduardo W. Basso, Ana L. C. Bazzan, Paulo M. Engel

This student abstract describes ongoing investigations regarding an approach for dealing with non-stationarity in reinforcement learning (RL) problems. We briefly propose and describe a method for managing multiple partial models of the environment and comment previous results which show that the proposed mechanism has better convergence times comparing to standard RL algorithms. Current efforts include the development of a more robust approach, capable of dealing with noisy environments, and also investigations regarding the possibility of using partial models in order to aliviate learning problems in systems with an explosive number of states.

Subjects: 12. Machine Learning and Discovery; 12.1 Reinforcement Learning


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