Aravind Rajeswaran

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University of Washington

COMBO: Conservative Offline Model-Based Policy Optimization

Model-based algorithms, which learn a dynamics model from logged experie...
02/16/2021 ∙ by Tianhe Yu, et al. ∙ 0 ∙ share

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Offline Reinforcement Learning from Images with Latent Space Models

Offline reinforcement learning (RL) refers to the problem of learning po...
12/21/2020 ∙ by Rafael Rafailov, et al. ∙ 10 ∙ share

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MOReL : Model-Based Offline Reinforcement Learning

In offline reinforcement learning (RL), the goal is to learn a successfu...
05/12/2020 ∙ by Rahul Kidambi, et al. ∙ 9 ∙ share

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A Game Theoretic Framework for Model Based Reinforcement Learning

Model-based reinforcement learning (MBRL) has recently gained immense in...
04/16/2020 ∙ by Aravind Rajeswaran, et al. ∙ 38 ∙ share

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Lyceum: An efficient and scalable ecosystem for robot learning

We introduce Lyceum, a high-performance computational ecosystem for robo...
01/21/2020 ∙ by Colin Summers, et al. ∙ 9 ∙ share

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Meta-Learning with Implicit Gradients

A core capability of intelligent systems is the ability to quickly learn...
09/10/2019 ∙ by Aravind Rajeswaran, et al. ∙ 2 ∙ share

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Online Meta-Learning

A central capability of intelligent systems is the ability to continuous...
02/22/2019 ∙ by Chelsea Finn, et al. ∙ 54 ∙ share

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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...
11/05/2018 ∙ by Kendall Lowrey, et al. ∙ 4 ∙ share

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

Dexterous multi-fingered robotic hands can perform a wide range of manip...
10/14/2018 ∙ by Henry Zhu, et al. ∙ 0 ∙ share

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

Reinforcement learning has emerged as a promising methodology for traini...
03/28/2018 ∙ by Kendall Lowrey, et al. ∙ 0 ∙ share

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Variance Reduction for Policy Gradient with Action-Dependent Factorized Baselines

Policy gradient methods have enjoyed great success in deep reinforcement...
03/20/2018 ∙ by Cathy Wu, et al. ∙ 0 ∙ share

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Divide-and-Conquer Reinforcement Learning

Standard model-free deep reinforcement learning (RL) algorithms sample a...
11/27/2017 ∙ by Dibya Ghosh, et al. ∙ 0 ∙ share

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Learning Complex Dexterous Manipulation with Deep Reinforcement Learning and Demonstrations

Dexterous multi-fingered hands are extremely versatile and provide a gen...
09/28/2017 ∙ by Aravind Rajeswaran, et al. ∙ 0 ∙ share

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EPOpt: Learning Robust Neural Network Policies Using Model Ensembles

Sample complexity and safety are major challenges when learning policies...
10/05/2016 ∙ by Aravind Rajeswaran, et al. ∙ 0 ∙ share

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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...
11/19/2015 ∙ by P Satya Jayadev, et al. ∙ 0 ∙ share

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Aravind Rajeswaran

Featured Co-authors

Sergey Levine

Pieter Abbeel

Chelsea Finn

Abhishek Gupta

Balaraman Ravindran

Praneeth Netrapalli

Sham Kakade

Thorsten Joachims

Igor Mordatch

Siddhartha Srinivasa

John Schulman

COMBO: Conservative Offline Model-Based Policy Optimization

Offline Reinforcement Learning from Images with Latent Space Models

MOReL : Model-Based Offline Reinforcement Learning

A Game Theoretic Framework for Model Based Reinforcement Learning

Lyceum: An efficient and scalable ecosystem for robot learning

Meta-Learning with Implicit Gradients

Online Meta-Learning

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

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

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

Variance Reduction for Policy Gradient with Action-Dependent Factorized Baselines

Divide-and-Conquer Reinforcement Learning

Learning Complex Dexterous Manipulation with Deep Reinforcement Learning and Demonstrations

EPOpt: Learning Robust Neural Network Policies Using Model Ensembles

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