465 citations · 4.2k across the 111 of their papers we have counts for
37 papers · 1 filter
Hierarchical Variational Imitation Learning of Control Programs
Roy Fox, Richard Shin, William Paul +5
Autonomous agents can learn by imitating teacher demonstrations of the intended behavior. Hierarchical control policies are ubiquitously useful for such learning, having the potent…
Predictive Coding for Boosting Deep Reinforcement Learning with Sparse Rewards
Xingyu Lu, Stas Tiomkin, Pieter Abbeel
While recent progress in deep reinforcement learning has enabled robots to learn complex behaviors, tasks with long horizons and sparse rewards remain an ongoing challenge. In this…
Natural Image Manipulation for Autoregressive Models Using Fisher Scores
Wilson Yan, Jonathan Ho, Pieter Abbeel
Deep autoregressive models are one of the most powerful models that exist today which achieve state-of-the-art bits per dim. However, they lie at a strict disadvantage when it come…
AVID: Learning Multi-Stage Tasks via Pixel-Level Translation of Human Videos
Laura Smith, Nikita Dhawan, Marvin Zhang +2
Robotic reinforcement learning (RL) holds the promise of enabling robots to learn complex behaviors through experience. However, realizing this promise for long-horizon tasks in th…
Learning Efficient Representation for Intrinsic Motivation
Ruihan Zhao, Stas Tiomkin, Pieter Abbeel
Mutual Information between agent Actions and environment States (MIAS) quantifies the influence of agent on its environment. Recently, it was found that the maximization of MIAS ca…
Adaptive Online Planning for Continual Lifelong Learning
Kevin Lu, Igor Mordatch, Pieter Abbeel
We study learning control in an online reset-free lifelong learning scenario, where mistakes can compound catastrophically into the future and the underlying dynamics of the enviro…