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NEARL: Non-Explicit Action Reinforcement Learning for Robotic Control
[article]
2020
arXiv
pre-print
Traditionally, reinforcement learning methods predict the next action based on the current state. However, in many situations, directly applying actions to control systems or robots is dangerous and may lead to unexpected behaviors because action is rather low-level. In this paper, we propose a novel hierarchical reinforcement learning framework without explicit action. Our meta policy tries to manipulate the next optimal state and actual action is produced by the inverse dynamics model. To
arXiv:2011.01046v1
fatcat:bicjlzj2wjffnji5wde5ttvymy