228 citations · 1.5k across the 29 of their papers we have counts for
29 papers
Reinforcement Learning with Competitive Ensembles of Information-Constrained Primitives
Anirudh Goyal, Shagun Sodhani, Jonathan Binas +3
Reinforcement learning agents that operate in diverse and complex environments can benefit from the structured decomposition of their behavior. Often, this is addressed in the cont…
Search on the Replay Buffer: Bridging Planning and Reinforcement Learning
Benjamin Eysenbach, Ruslan Salakhutdinov, Sergey Levine
The history of learning for control has been an exciting back and forth between two broad classes of algorithms: planning and reinforcement learning. Planning algorithms effectivel…
Learning Powerful Policies by Using Consistent Dynamics Model
Shagun Sodhani, Anirudh Goyal, Tristan Deleu +3
Model-based Reinforcement Learning approaches have the promise of being sample efficient. Much of the progress in learning dynamics models in RL has been made by learning models vi…
MCP: Learning Composable Hierarchical Control with Multiplicative Compositional Policies
Xue Bin Peng, Michael Chang, Grace Zhang +2
Humans are able to perform a myriad of sophisticated tasks by drawing upon skills acquired through prior experience. For autonomous agents to have this capability, they must be abl…
REPLAB: A Reproducible Low-Cost Arm Benchmark Platform for Robotic Learning
Brian Yang, Jesse Zhang, Vitchyr Pong +2
Standardized evaluation measures have aided in the progress of machine learning approaches in disciplines such as computer vision and machine translation. In this paper, we make th…
End-to-End Robotic Reinforcement Learning without Reward Engineering
Avi Singh, Larry Yang, Kristian Hartikainen +2
The combination of deep neural network models and reinforcement learning algorithms can make it possible to learn policies for robotic behaviors that directly read in raw sensory i…