4 papers · 1 filter
Graphon Mean-Field Subsampling for Cooperative Heterogeneous Multi-Agent Reinforcement Learning
Emile Anand, Richard Hoffmann, Sarah Liaw +1
Coordinating large populations of interacting agents is a central challenge in multi-agent reinforcement learning (MARL), where the size of the joint state-action space scales expo…
Feel-Good Thompson Sampling for Contextual Bandits: a Markov Chain Monte Carlo Showdown
Emile Anand, Sarah Liaw
Thompson Sampling (TS) is widely used to address the exploration/exploitation tradeoff in contextual bandits, yet recent theory shows that it does not explore aggressively enough i…
Mean-Field Sampling for Cooperative Multi-Agent Reinforcement Learning
Emile Anand, Ishani Karmarkar, Guannan Qu
Designing efficient algorithms for multi-agent reinforcement learning (MARL) is fundamentally challenging because the size of the joint state and action spaces grows exponentially…
Peer-to-Peer Learning Dynamics of Wide Neural Networks
Shreyas Chaudhari, Srinivasa Pranav, Emile Anand +1
Peer-to-peer learning is an increasingly popular framework that enables beyond-5G distributed edge devices to collaboratively train deep neural networks in a privacy-preserving man…