Search Results for author: Albert Zhan

Found 4 papers, 2 papers with code

URLB: Unsupervised Reinforcement Learning Benchmark

1 code implementation28 Oct 2021 Michael Laskin, Denis Yarats, Hao liu, Kimin Lee, Albert Zhan, Kevin Lu, Catherine Cang, Lerrel Pinto, Pieter Abbeel

Deep Reinforcement Learning (RL) has emerged as a powerful paradigm to solve a range of complex yet specific control tasks.

Continuous Control reinforcement-learning +2

Hierarchical Few-Shot Imitation with Skill Transition Models

1 code implementation ICML Workshop URL 2021 Kourosh Hakhamaneshi, Ruihan Zhao, Albert Zhan, Pieter Abbeel, Michael Laskin

To this end, we present Few-shot Imitation with Skill Transition Models (FIST), an algorithm that extracts skills from offline data and utilizes them to generalize to unseen tasks given a few downstream demonstrations.

Learning Visual Robotic Control Efficiently with Contrastive Pre-training and Data Augmentation

no code implementations14 Dec 2020 Albert Zhan, Ruihan Zhao, Lerrel Pinto, Pieter Abbeel, Michael Laskin

We present Contrastive Pre-training and Data Augmentation for Efficient Robotic Learning (CoDER), a method that utilizes data augmentation and unsupervised learning to achieve sample-efficient training of real-robot arm policies from sparse rewards.

Data Augmentation reinforcement-learning +2

Preventing Imitation Learning with Adversarial Policy Ensembles

no code implementations31 Jan 2020 Albert Zhan, Stas Tiomkin, Pieter Abbeel

To our knowledge, this is the first work regarding the protection of policies in Reinforcement Learning.

Imitation Learning reinforcement-learning +1

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