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Misha Laskin

2 accepted papers

2020

Reinforcement Learning with Augmented Data

NeurIPS 2020spotlight

Learning from visual observations is a fundamental yet challenging problem in Reinforcement Learning (RL). Although algorithmic advances combined with convolutional neural networks have proved to be a recipe for success, current methods are still lacking on two fronts: (a) data-efficiency of learnin…

2020

Sparse Graphical Memory for Robust Planning

NeurIPS 2020poster

To operate effectively in the real world, agents should be able to act from high-dimensional raw sensory input such as images and achieve diverse goals across long time-horizons. Current deep reinforcement and imitation learning methods can learn directly from high-dimensional inputs but do not sca…