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Value constrained model-free continuous control
[article]
2019
arXiv
pre-print
The naive application of Reinforcement Learning algorithms to continuous control problems -- such as locomotion and manipulation -- often results in policies which rely on high-amplitude, high-frequency control signals, known colloquially as bang-bang control. Although such solutions may indeed maximize task reward, they can be unsuitable for real world systems. Bang-bang control may lead to increased wear and tear or energy consumption, and tends to excite undesired second-order dynamics. To
arXiv:1902.04623v1
fatcat:7cwedpwtjneg3jotb5xmk2ay5i