2 citations · 2 across the 1 of their papers we have counts for
4 papers · 1 filter
JaxMARL: Multi-Agent RL Environments and Algorithms in JAX
Alexander Rutherford, Benjamin Ellis, Matteo Gallici +18
Benchmarks are crucial in the development of machine learning algorithms, with available environments significantly influencing reinforcement learning (RL) research. Traditionally,…
No Regrets: Investigating and Improving Regret Approximations for Curriculum Discovery
Alexander Rutherford, Michael Beukman, Timon Willi +3
What data or environments to use for training to improve downstream performance is a longstanding and very topical question in reinforcement learning. In particular, Unsupervised E…
Mixture of Experts in a Mixture of RL settings
Timon Willi, Johan Obando-Ceron, Jakob Foerster +2
Mixtures of Experts (MoEs) have gained prominence in (self-)supervised learning due to their enhanced inference efficiency, adaptability to distributed training, and modularity. Pr…
Mixtures of Experts Unlock Parameter Scaling for Deep RL
Johan Obando-Ceron, Ghada Sokar, Timon Willi +6
The recent rapid progress in (self) supervised learning models is in large part predicted by empirical scaling laws: a model's performance scales proportionally to its size. Analog…