907 citations · 1.3k across the 17 of their papers we have counts for
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Evaluating Vision Transformer Methods for Deep Reinforcement Learning from Pixels
Tianxin Tao, Daniele Reda, Michiel van de Panne
Vision Transformers (ViT) have recently demonstrated the significant potential of transformer architectures for computer vision. To what extent can image-based deep reinforcement l…
Learning to Brachiate via Simplified Model Imitation
Daniele Reda, Hung Yu Ling, Michiel van de Panne
Brachiation is the primary form of locomotion for gibbons and siamangs, in which these primates swing from tree limb to tree limb using only their arms. It is challenging to contro…
Exploration with Multi-Sample Target Values for Distributional Reinforcement Learning
Michael Teng, Michiel van de Panne, Frank Wood
Distributional reinforcement learning (RL) aims to learn a value-network that predicts the full distribution of the returns for a given state, often modeled via a quantile-based cr…
Discovering Diverse Athletic Jumping Strategies
Zhiqi Yin, Zeshi Yang, Michiel van de Panne +1
We present a framework that enables the discovery of diverse and natural-looking motion strategies for athletic skills such as the high jump. The strategies are realized as control…
Character Controllers Using Motion VAEs
Hung Yu Ling, Fabio Zinno, George Cheng +1
A fundamental problem in computer animation is that of realizing purposeful and realistic human movement given a sufficiently-rich set of motion capture clips. We learn data-driven…
Learning to Locomote: Understanding How Environment Design Matters for Deep Reinforcement Learning
Daniele Reda, Tianxin Tao, Michiel van de Panne
Learning to locomote is one of the most common tasks in physics-based animation and deep reinforcement learning (RL). A learned policy is the product of the problem to be solved, a…
Progressive Reinforcement Learning with Distillation for Multi-Skilled Motion Control
Glen Berseth, Cheng Xie, Paul Cernek +1
Deep reinforcement learning has demonstrated increasing capabilities for continuous control problems, including agents that can move with skill and agility through their environmen…