The existing reinforcement learning methods have been seriously suffering from the curse of dimension problem especially when they are applied to multiagent dynamic environments. ...
Abstract. This paper proposes a fuzzy-rough method of maintaining CaseBased Reasoning (CBR) systems. The methodology is mainly based on the idea that a large case library can be tr...
The application of reinforcement learning algorithms to Partially Observable Stochastic Games (POSG) is challenging since each agent does not have access to the whole state inform...
Alessandro Lazaric, Mario Quaresimale, Marcello Re...
Dynamic Programming, Q-learning and other discrete Markov Decision Process solvers can be applied to continuous d-dimensional state-spaces by quantizing the state space into an arr...
Reinforcement techniques have been successfully used to maximise the expected cumulative reward of statistical dialogue systems. Typically, reinforcement learning is used to estim...