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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Vydané v:	IEEE robotics and automation letters Ročník 7; číslo 2; s. 1379 - 1386
Hlavní autori:	Ren, Allen Z., Majumdar, Anirudha
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Piscataway IEEE 01.04.2022 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:	continual learning Costs data sets for robot learning generalization Grasping Optimization Policies Reinforcement learning Robots Robustness Task analysis Training
ISSN:	2377-3766, 2377-3766
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