Reward criteria impact on the performance of reinforcement learning agent for autonomous navigation

In reinforcement learning, an agent takes action at every time step (follows a policy) in an environment to maximize the expected cumulative reward. Therefore, the shaping of a reward function plays a crucial role in an agent’s learning. Designing an optimal reward function is not a trivial task. In...

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Veröffentlicht in:	Applied soft computing Jg. 126; S. 109241
Hauptverfasser:	Dayal, Aveen, Cenkeramaddi, Linga Reddy, Jha, Ajit
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Elsevier B.V 01.09.2022
Schlagworte:	Autonomous navigation Deep reinforcement learning Machine learning and artificial intelligence Reward criteria Deep reinforcement learning Autonomous navigation Reward criteria Machine learning and artificial intelligence
ISSN:	1568-4946, 1872-9681
Online-Zugang:	Volltext
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