Russell Mendonca | DeepAI

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∙ 09/05/2023

Efficient RL via Disentangled Environment and Agent Representations

Agents that are aware of the separation between themselves and their env...

0 Kevin Gmelin, et al. ∙

∙ 08/21/2023

Structured World Models from Human Videos

We tackle the problem of learning complex, general behaviors directly in...

0 Russell Mendonca, et al. ∙

∙ 04/17/2023

Affordances from Human Videos as a Versatile Representation for Robotics

Building a robot that can understand and learn to interact by watching h...

0 Shikhar Bahl, et al. ∙

∙ 02/13/2023

ALAN: Autonomously Exploring Robotic Agents in the Real World

Robotic agents that operate autonomously in the real world need to conti...

0 Russell Mendonca, et al. ∙

∙ 10/18/2021

Discovering and Achieving Goals via World Models

How can artificial agents learn to solve many diverse tasks in complex v...

8 Russell Mendonca, et al. ∙

∙ 06/12/2020

Meta-Reinforcement Learning Robust to Distributional Shift via Model Identification and Experience Relabeling

Reinforcement learning algorithms can acquire policies for complex tasks...

41 Russell Mendonca, et al. ∙

∙ 04/01/2019

Guided Meta-Policy Search

Reinforcement learning (RL) algorithms have demonstrated promising resul...

8 Russell Mendonca, et al. ∙

∙ 02/20/2018

Meta-Reinforcement Learning of Structured Exploration Strategies

Exploration is a fundamental challenge in reinforcement learning (RL). M...

0 Abhishek Gupta, et al. ∙

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