8 citations · 21 across the 13 of their papers we have counts for
28 papers
Adam on Local Time: Addressing Nonstationarity in RL with Relative Adam Timesteps
Benjamin Ellis, Matthew T. Jackson, Andrei Lupu +4
In reinforcement learning (RL), it is common to apply techniques used broadly in machine learning such as neural network function approximators and momentum-based optimizers. Howev…
Noisy Zero-Shot Coordination: Breaking The Common Knowledge Assumption In Zero-Shot Coordination Games
Usman Anwar, Ashish Pandian, Jia Wan +2
Zero-shot coordination (ZSC) is a popular setting for studying the ability of reinforcement learning (RL) agents to coordinate with novel partners. Prior ZSC formulations assume th…
Beyond the Boundaries of Proximal Policy Optimization
Charlie B. Tan, Edan Toledo, Benjamin Ellis +2
Proximal policy optimization (PPO) is a widely-used algorithm for on-policy reinforcement learning. This work offers an alternative perspective of PPO, in which it is decomposed in…
BAM! Just Like That: Simple and Efficient Parameter Upcycling for Mixture of Experts
Qizhen Zhang, Nikolas Gritsch, Dwaraknath Gnaneshwar +8
The Mixture of Experts (MoE) framework has become a popular architecture for large language models due to its superior performance over dense models. However, training MoEs from sc…
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…
Behaviour Distillation
Andrei Lupu, Chris Lu, Jarek Liesen +2
Dataset distillation aims to condense large datasets into a small number of synthetic examples that can be used as drop-in replacements when training new models. It has application…