113 citations · 242 across the 6 of their papers we have counts for
6 papers
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…
When to Trust Your Model: Model-Based Policy Optimization
Michael Janner, Justin Fu, Marvin Zhang +1
Designing effective model-based reinforcement learning algorithms is difficult because the ease of data generation must be weighed against the bias of model-generated data. In this…
Unsteady ballistic heat transport in infinite harmonic crystals
Vitaly A. Kuzkin
We study thermal processes in infinite harmonic crystals having a unit cell with arbitrary number of particles. Initially particles have zero displacements and random velocities, c…
Combining Model-Based and Model-Free Updates for Trajectory-Centric Reinforcement Learning
Yevgen Chebotar, Karol Hausman, Marvin Zhang +3
Reinforcement learning (RL) algorithms for real-world robotic applications need a data-efficient learning process and the ability to handle complex, unknown dynamical systems. Thes…
Deep Reinforcement Learning for Tensegrity Robot Locomotion
Marvin Zhang, Xinyang Geng, Jonathan Bruce +5
Tensegrity robots, composed of rigid rods connected by elastic cables, have a number of unique properties that make them appealing for use as planetary exploration rovers. However,…
Learning Deep Neural Network Policies with Continuous Memory States
Marvin Zhang, Zoe McCarthy, Chelsea Finn +2
Policy learning for partially observed control tasks requires policies that can remember salient information from past observations. In this paper, we present a method for learning…