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Ksenia Konyushova

2 accepted papers

2022

Retrieval-Augmented Reinforcement Learning

ICML 2022spotlight

Most deep reinforcement learning (RL) algorithms distill experience into parametric behavior policies or value functions via gradient updates. While effective, this approach has several disadvantages: (1) it is computationally expensive, (2) it can take many updates to integrate experiences into the…

Cited by 59SourcePDFScholar
2020

Scaling data-driven robotics with reward sketching and batch reinforcement learning

RSS 2020poster

By harnessing a growing dataset of robot experience, we learn control policies for a diverse and increasing set of related manipulation tasks. To make this possible, we introduce reward sketching: an effective way of eliciting human preferences to learn the reward function for a new task. This rewar…