Distributionally Robust Policy Learning via Adversarial Environment Generation

Our goal is to train control policies that generalize well to unseen environments. Inspired by the Distributionally Robust Optimization (DRO) framework, we propose DRAGEN - Distributionally Robust policy learning via Adversarial Generation of ENvironments - for iteratively improving robustness of po...

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Veröffentlicht in:	IEEE robotics and automation letters Jg. 7; H. 2; S. 1379 - 1386
Hauptverfasser:	Ren, Allen Z., Majumdar, Anirudha
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Piscataway IEEE 01.04.2022 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	continual learning Costs data sets for robot learning generalization Grasping Optimization Policies Reinforcement learning Robots Robustness Task analysis Training
ISSN:	2377-3766, 2377-3766
Online-Zugang:	Volltext
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