Enhanced robot learning using fuzzy Q-Learning & context-aware middleware

In this paper we continue with previous work by the authors implementing context-aware middleware to accelerate robot learning from demonstration, LfD. Specifically, we apply Fuzzy Q-Learning, FQL, reinforcement learning strategy to enhance the learning experience of the robot. Typically, fuzzy techniques allow the robot to make decisions without the need for an exhaustive map of the world. FQL, approximates the observable configuration space allowing the robot to overcome the high dimension challenge of feature decomposition and navigation in a stochastic environment.