12 citations · 16 across the 3 of their papers we have counts for
6 papers
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…
Imagining The Road Ahead: Multi-Agent Trajectory Prediction via Differentiable Simulation
Adam Scibior, Vasileios Lioutas, Daniele Reda +2
We develop a deep generative model built on a fully differentiable simulator for multi-agent trajectory prediction. Agents are modeled with conditional recurrent variational neural…
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…
Urban Driving with Conditional Imitation Learning
Jeffrey Hawke, Richard Shen, Corina Gurau +8
Hand-crafting generalised decision-making rules for real-world urban autonomous driving is hard. Alternatively, learning behaviour from easy-to-collect human driving demonstrations…
Learning to Drive in a Day
Alex Kendall, Jeffrey Hawke, David Janz +6
We demonstrate the first application of deep reinforcement learning to autonomous driving. From randomly initialised parameters, our model is able to learn a policy for lane follow…