首页|Curriculum learning with Hindsight Experience Replay for sequential object manipulation tasks
Curriculum learning with Hindsight Experience Replay for sequential object manipulation tasks
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NSTL
Elsevier
? 2021 Elsevier LtdLearning complex tasks from scratch is challenging and often impossible for humans as well as for artificial agents. Instead, a curriculum can be used, which decomposes a complex task – the target task – into a sequence of source tasks. Each source task is a simplified version of the next source task with increasing complexity. Learning then occurs gradually by training on each source task while using knowledge from the curriculum's prior source tasks. In this study, we present a new algorithm that combines curriculum learning with Hindsight Experience Replay (HER), to learn sequential object manipulation tasks for multiple goals and sparse feedback. The algorithm exploits the recurrent structure inherent in many object manipulation tasks and implements the entire learning process in the original simulation without adjusting it to each source task. We test our algorithm on three challenging throwing tasks in simulation and show significant improvements compared to vanilla-HER.
Curriculum learningHindsight Experience ReplayMulti-goal reinforcement learningObject manipulation tasksSparse reward function
Manela B.、Biess A.
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Department of Industrial Engineering and Management Ben-Gurion University of the Negev
NSTL
Elsevier
