1k citations · 4.2k across the 34 of their papers we have counts for
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Reset-Free Lifelong Learning with Skill-Space Planning
Kevin Lu, Aditya Grover, Pieter Abbeel +1
The objective of lifelong reinforcement learning (RL) is to optimize agents which can continuously adapt and interact in changing environments. However, current RL approaches fail…
Rearrangement: A Challenge for Embodied AI
Dhruv Batra, Angel X. Chang, Sonia Chernova +9
We describe a framework for research and evaluation in Embodied AI. Our proposal is based on a canonical task: Rearrangement. A standard task can focus the development of new techn…
Generative Temporal Difference Learning for Infinite-Horizon Prediction
Michael Janner, Igor Mordatch, Sergey Levine
We introduce the -model, a predictive model of environment dynamics with an infinite probabilistic horizon. Replacing standard single-step models with -models leads to genera…
One Policy to Control Them All: Shared Modular Policies for Agent-Agnostic Control
Wenlong Huang, Igor Mordatch, Deepak Pathak
Reinforcement learning is typically concerned with learning control policies tailored to a particular agent. We investigate whether there exists a single global policy that can gen…
A Game Theoretic Framework for Model Based Reinforcement Learning
Aravind Rajeswaran, Igor Mordatch, Vikash Kumar
Model-based reinforcement learning (MBRL) has recently gained immense interest due to its potential for sample efficiency and ability to incorporate off-policy data. However, desig…