668 citations · 2.5k across the 29 of their papers we have counts for
29 papers
Deep Dive into Model-free Reinforcement Learning for Biological and Robotic Systems: Theory and Practice
Yusheng Jiao, Feng Ling, Sina Heydari +3
Animals and robots exist in a physical world and must coordinate their bodies to achieve behavioral objectives. With recent developments in deep reinforcement learning, it is now p…
Data augmentation for efficient learning from parametric experts
Alexandre Galashov, Josh Merel, Nicolas Heess
We present a simple, yet powerful data-augmentation technique to enable data-efficient learning from parametric experts for reinforcement and imitation learning. We focus on what w…
Imitate and Repurpose: Learning Reusable Robot Movement Skills From Human and Animal Behaviors
Steven Bohez, Saran Tunyasuvunakool, Philemon Brakel +18
We investigate the use of prior knowledge of human and animal movement to learn reusable locomotion skills for real legged robots. Our approach builds upon previous work on imitati…
NeuPL: Neural Population Learning
Siqi Liu, Luke Marris, Daniel Hennes +3
Learning in strategy games (e.g. StarCraft, poker) requires the discovery of diverse policies. This is often achieved by iteratively training new policies against existing ones, gr…
Learning Transferable Motor Skills with Hierarchical Latent Mixture Policies
Dushyant Rao, Fereshteh Sadeghi, Leonard Hasenclever +8
For robots operating in the real world, it is desirable to learn reusable behaviours that can effectively be transferred and adapted to numerous tasks and scenarios. We propose an…
Divergent representations of ethological visual inputs emerge from supervised, unsupervised, and reinforcement learning
Grace W. Lindsay, Josh Merel, Tom Mrsic-Flogel +1
Artificial neural systems trained using reinforcement, supervised, and unsupervised learning all acquire internal representations of high dimensional input. To what extent these re…