6.3k citations
- University of California, BerkeleyUS18 papers
- Google (United States)US11 papers
- Stanford UniversityUS8 papers
- Carnegie Mellon UniversityUS6 papers
- Berkeley CollegeUS4 papers
- Uber AI (United States)US4 papers
- ETH ZurichCH3 papers
- Institut national de recherche en sciences et technologies du numériqueFR3 papers
- McGill UniversityCA3 papers
- Microsoft (United States)US3 papers
- Mila - Quebec Artificial Intelligence InstituteCA3 papers
- The University of Texas at AustinUS3 papers
12 papers · 1 filter
Sim-to-Real Transfer of Robotic Control with Dynamics Randomization
Xue Bin Peng, Marcin Andrychowicz, Wojciech Zaremba +1
Simulations are attractive environments for training agents as they provide an abundant source of data and alleviate certain safety concerns during the training process. But the be…
3D Sketching using Multi-View Deep Volumetric Prediction
Johanna Delanoy, Mathieu Aubry, Phillip Isola +2
Sketch-based modeling strives to bring the ease and immediacy of drawing to the 3D world. However, while drawings are easy for humans to create, they are very challenging for compu…
Imitation from Observation: Learning to Imitate Behaviors from Raw Video via Context Translation
YuXuan Liu, Abhishek Gupta, Pieter Abbeel +1
Imitation learning is an effective approach for autonomous systems to acquire control policies when an explicit reward function is unavailable, using supervision provided as demons…
Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments
Ryan Lowe, Yi Wu, Aviv Tamar +3
We explore deep reinforcement learning methods for multi-agent domains. We begin by analyzing the difficulty of traditional algorithms in the multi-agent case: Q-learning is challe…
Constrained Policy Optimization
Joshua Achiam, David Held, Aviv Tamar +1
For many applications of reinforcement learning it can be more convenient to specify both a reward function and constraints, rather than trying to design behavior through the rewar…
Domain Randomization for Transferring Deep Neural Networks from Simulation to the Real World
Josh Tobin, Rachel Fong, Alex Ray +3
Bridging the 'reality gap' that separates simulated robotics from experiments on hardware could accelerate robotic research through improved data availability. This paper explores…