10.1k citations · 13.3k across the 33 of their papers we have counts for
6 papers · 1 filter
From Motor Control to Team Play in Simulated Humanoid Football
Siqi Liu, Guy Lever, Zhe Wang +19
Intelligent behaviour in the physical world exhibits structure at multiple spatial and temporal scales. Although movements are ultimately executed at the level of instantaneous mus…
Catch & Carry: Reusable Neural Controllers for Vision-Guided Whole-Body Tasks
Josh Merel, Saran Tunyasuvunakool, Arun Ahuja +6
We address the longstanding challenge of producing flexible, realistic humanoid character controllers that can perform diverse whole-body tasks involving object interactions. This…
Learning Awareness Models
Brandon Amos, Laurent Dinh, Serkan Cabi +7
We consider the setting of an agent with a fixed body interacting with an unknown and uncertain external world. We show that models trained to predict proprioceptive information ab…
Safe Exploration in Continuous Action Spaces
Gal Dalal, Krishnamurthy Dvijotham, Matej Vecerik +3
We address the problem of deploying a reinforcement learning (RL) agent on a physical system such as a datacenter cooling unit or robot, where critical constraints must never be vi…
DeepMind Control Suite
Yuval Tassa, Yotam Doron, Alistair Muldal +9
The DeepMind Control Suite is a set of continuous control tasks with a standardised structure and interpretable rewards, intended to serve as performance benchmarks for reinforceme…
Emergence of Locomotion Behaviours in Rich Environments
Nicolas Heess, Dhruva TB, Srinivasan Sriram +9
The reinforcement learning paradigm allows, in principle, for complex behaviours to be learned directly from simple reward signals. In practice, however, it is common to carefully…