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- W1458663242 abstract "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)." @default.
- W1458663242 created "2016-06-24" @default.
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- W1458663242 date "2015-01-01" @default.
- W1458663242 modified "2023-10-13" @default.
- W1458663242 title "Reinforcement learning based techniques in uncertain environments: problems and solutions" @default.
- W1458663242 hasPublicationYear "2015" @default.
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