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Hierarchical Reinforcement Learning Using Automatic Task Decomposition And Exploration Shaping
(Computer Science & Engineering, 2008-08-08)
Reinforcement learning agents situated in real world environments have to be able to address a number of challenges in order to succeed at accomplishing a wide range of tasks over their lifetime. Among these, such systems ...
LEARNING TRANSFERABLE META-POLICIES FOR HIERARCHICAL TASK DECOMPOSITION AND PLANNING COMPOSITION
(2019-12-16)
In real world scenarios where situated agents are faced with dynamic, high-dimensional, partially observable environments with action and reward uncertainty, the traditional states space Reinforcement Learning (RL) becomes ...