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A Deep Hierarchical Reinforcement Learning Algorithm in Partially Observable Markov Decision Processes
2018
IEEE Access
In recent years, reinforcement learning has achieved many remarkable successes due to the growing adoption of deep learning techniques and the rapid growth in computing power. Nevertheless, it is well-known that flat reinforcement learning algorithms are often not able to learn well and data-efficient in tasks having hierarchical structures, e.g. consisting of multiple subtasks. Hierarchical reinforcement learning is a principled approach that is able to tackle these challenging tasks. On the
doi:10.1109/access.2018.2854283
fatcat:rffxlckxcjg2fkucnqy53b64x4