3 citations · 6 across the 11 of their papers we have counts for
5 papers · 1 filter
Efficient Morphology-Control Co-Design via Stackelberg Proximal Policy Optimization
Yanning Dai, Yuhui Wang, Dylan R. Ashley +1
Morphology-control co-design concerns the coupled optimization of an agent's body structure and control policy. This problem exhibits a bi-level structure, where the control dynami…
Upside Down Reinforcement Learning with Policy Generators
Jacopo Di Ventura, Dylan R. Ashley, Vincent Herrmann +2
Upside Down Reinforcement Learning (UDRL) is a promising framework for solving reinforcement learning problems which focuses on learning command-conditioned policies. In this work,…
Automatic Album Sequencing
Vincent Herrmann, Dylan R. Ashley, Jürgen Schmidhuber
Album sequencing is a critical part of the album production process. Recently, a data-driven approach was proposed that sequences general collections of independent media by extrac…
Scaling Value Iteration Networks to 5000 Layers for Extreme Long-Term Planning
Yuhui Wang, Qingyuan Wu, Dylan R. Ashley +4
The Value Iteration Network (VIN) is an end-to-end differentiable neural network architecture for planning. It exhibits strong generalization to unseen domains by incorporating a d…
The Languini Kitchen: Enabling Language Modelling Research at Different Scales of Compute
Aleksandar Stanić, Dylan Ashley, Oleg Serikov +5
The Languini Kitchen serves as both a research collective and codebase designed to empower researchers with limited computational resources to contribute meaningfully to the field…