6 citations · 7 across the 6 of their papers we have counts for
6 papers
Finding the Time to Think: Learning Planning Budgets in Real-Time RL
Aneesh Muppidi, Firas Darwish, Dylan Cope +2
Deliberating takes time. In real-time settings, that time is not free. Standard reinforcement learning (RL) sidesteps this as the environment waits indefinitely for the agent's dec…
Multi-Agent Craftax: Benchmarking Open-Ended Multi-Agent Reinforcement Learning at the Hyperscale
Bassel Al Omari, Michael Matthews, Alexander Rutherford +1
Progress in multi-agent reinforcement learning (MARL) requires challenging benchmarks that assess the limits of current methods. However, existing benchmarks often target narrow sh…
An Optimisation Framework for Unsupervised Environment Design
Nathan Monette, Alistair Letcher, Michael Beukman +4
For reinforcement learning agents to be deployed in high-risk settings, they must achieve a high level of robustness to unfamiliar scenarios. One method for improving robustness is…
Reinforcement Learning Controllers for Soft Robots using Learned Environments
Uljad Berdica, Matthew Jackson, Niccolò Enrico Veronese +2
Soft robotic manipulators offer operational advantage due to their compliant and deformable structures. However, their inherently nonlinear dynamics presents substantial challenges…
Kinetix: Investigating the Training of General Agents through Open-Ended Physics-Based Control Tasks
Michael Matthews, Michael Beukman, Chris Lu +1
While large models trained with self-supervised learning on offline datasets have shown remarkable capabilities in text and image domains, achieving the same generalisation for age…
JaxLife: An Open-Ended Agentic Simulator
Chris Lu, Michael Beukman, Michael Matthews +1
Human intelligence emerged through the process of natural selection and evolution on Earth. We investigate what it would take to re-create this process in silico. While past work h…