8 papers
DiscoGen: Procedural Generation of Algorithm Discovery Tasks in Machine Learning
Alexander D. Goldie, Zilin Wang, Adrian Hayler +17
Automating the development of machine learning algorithms has the potential to unlock new breakthroughs. However, our ability to improve and evaluate algorithm discovery systems ha…
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…
JAX-Based Batched AC Power Flow for GPU Acceleration and AI Ecosystem Integration
Yihong Zhou, Dylan Cope, Jakob Foerster +1
Coordinating growing grid flexibility under uncertainty is becoming increasingly important for efficient and reliable power-system operation. A core computational requirement is th…
Preventing Learning Stagnation in PPO by Scaling to 1 Million Parallel Environments
Michael Beukman, Khimya Khetarpal, Zeyu Zheng +4
An agent's performance stagnating at a suboptimal level is a common problem in deep on-policy RL. Focusing on PPO, we show that plateaus in certain regimes arise not because of kno…
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…