most citedTrajectory Planning with Deep Reinforcement Learning in High-Level Action Spaces

29 citations · 29 across the 1 of their papers we have counts for

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eess.SY2021★ 29 cited

Trajectory Planning with Deep Reinforcement Learning in High-Level Action Spaces

Kyle R. Williams, Rachel Schlossman, Daniel Whitten +8

This paper presents a technique for trajectory planning based on continuously parameterized high-level actions (motion primitives) of variable duration. This technique leverages de…