Reinforcement learning based techniques in uncertain environments: problems and solutions
Nouar AlDahoul, Rini Akmeliawati, Amir Akramin Shafie, Sheroz Khan · 2015
Reinforcement learning (RL) is a well-known class of machine learning algorithms used in planning and controlling of autonomous agents. Most of the issues in planning and controlling of robots are caused by uncertainties in the actuators and sensors of robots. The paper discusses important issues faced by RL in unknown and unstructured environments. It reviews problems of RL and solutions using different variants of RL namely: hierarchical RL, Bayesian model based learning, and Partially observable Markov decision processes (POMDP).