Open AccessUtilizing Reinforcement Learning to Continuously Improve a Primitive-Based Motion Planner.
Author(s) -
Zachary Goddard,
Kenneth Wardlaw,
Rohith Krishnan,
Panagiotis Tsiotras,
Michael R. Smith,
Mary Sena,
Julie Parish,
Anirban Mazumdar
Publication year - 2020
Publication title -
osti oai (u.s. department of energy office of scientific and technical information)
Language(s) - Uncategorized
Resource type - Conference proceedings
DOI - 10.2172/1836182
Subject(s) - heuristics , computer science , motion planning , reinforcement learning , planner , obstacle , task (project management) , path (computing) , motion (physics) , artificial intelligence , obstacle avoidance , nonlinear system , robot , computer vision , mobile robot , programming language , engineering , physics , systems engineering , quantum mechanics , political science , law , operating system
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