Aravind Rajeswaran

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University of Washington

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∙ 02/01/2022

CIC: Contrastive Intrinsic Control for Unsupervised Skill Discovery

We introduce Contrastive Intrinsic Control (CIC), an algorithm for unsup...

Michael (Misha) Laskin, et al. ∙

∙ 07/16/2021

Visual Adversarial Imitation Learning using Variational Models

Reward function specification, which requires considerable human effort ...

Rafael Rafailov, et al. ∙

∙ 06/16/2021

Behavioral Priors and Dynamics Models: Improving Performance and Domain Transfer in Offline RL

Offline Reinforcement Learning (RL) aims to extract near-optimal policie...

Catherine Cang, et al. ∙

∙ 06/02/2021

Decision Transformer: Reinforcement Learning via Sequence Modeling

We present a framework that abstracts Reinforcement Learning (RL) as a s...

Lili Chen, et al. ∙

∙ 02/16/2021

COMBO: Conservative Offline Model-Based Policy Optimization

Model-based algorithms, which learn a dynamics model from logged experie...

Tianhe Yu, et al. ∙

∙ 12/21/2020

Offline Reinforcement Learning from Images with Latent Space Models

Offline reinforcement learning (RL) refers to the problem of learning po...

Rafael Rafailov, et al. ∙

∙ 05/12/2020

MOReL : Model-Based Offline Reinforcement Learning

In offline reinforcement learning (RL), the goal is to learn a successfu...

Rahul Kidambi, et al. ∙

∙ 04/16/2020

A Game Theoretic Framework for Model Based Reinforcement Learning

Model-based reinforcement learning (MBRL) has recently gained immense in...

Aravind Rajeswaran, et al. ∙

∙ 01/21/2020

Lyceum: An efficient and scalable ecosystem for robot learning

We introduce Lyceum, a high-performance computational ecosystem for robo...

Colin Summers, et al. ∙

∙ 09/10/2019

Meta-Learning with Implicit Gradients

A core capability of intelligent systems is the ability to quickly learn...

Aravind Rajeswaran, et al. ∙

∙ 02/22/2019

Online Meta-Learning

A central capability of intelligent systems is the ability to continuous...

Chelsea Finn, et al. ∙

∙ 11/05/2018

Plan Online, Learn Offline: Efficient Learning and Exploration via Model-Based Control

We propose a plan online and learn offline (POLO) framework for the sett...

Kendall Lowrey, et al. ∙

∙ 10/14/2018

Dexterous Manipulation with Deep Reinforcement Learning: Efficient, General, and Low-Cost

Dexterous multi-fingered robotic hands can perform a wide range of manip...

Henry Zhu, et al. ∙

∙ 03/28/2018

Reinforcement learning for non-prehensile manipulation: Transfer from simulation to physical system

Reinforcement learning has emerged as a promising methodology for traini...

Kendall Lowrey, et al. ∙

∙ 03/20/2018

Variance Reduction for Policy Gradient with Action-Dependent Factorized Baselines

Policy gradient methods have enjoyed great success in deep reinforcement...

Cathy Wu, et al. ∙

∙ 11/27/2017

Divide-and-Conquer Reinforcement Learning

Standard model-free deep reinforcement learning (RL) algorithms sample a...

Dibya Ghosh, et al. ∙

∙ 09/28/2017

Learning Complex Dexterous Manipulation with Deep Reinforcement Learning and Demonstrations

Dexterous multi-fingered hands are extremely versatile and provide a gen...

Aravind Rajeswaran, et al. ∙

∙ 10/05/2016

EPOpt: Learning Robust Neural Network Policies Using Model Ensembles

Sample complexity and safety are major challenges when learning policies...

Aravind Rajeswaran, et al. ∙

∙ 11/19/2015

A Novel Approach for Phase Identification in Smart Grids Using Graph Theory and Principal Component Analysis

Consumers with low demand, like households, are generally supplied singl...

P Satya Jayadev, et al. ∙