35 citations · 36 across the 11 of their papers we have counts for
3 papers · 1 filter
How Does the Lagrangian Guide Safe Reinforcement Learning through Diffusion Models?
Xiaoyuan Cheng, Wenxuan Yuan, Boyang Li +7
Diffusion policy sampling enables reinforcement learning (RL) to represent multimodal action distributions beyond suboptimal unimodal Gaussian policies. However, existing diffusion…
On the Complexity of Learning to Cooperate with Populations of Socially Rational Agents
Robert Loftin, Saptarashmi Bandyopadhyay, Mustafa Mert Çelikok
Artificially intelligent agents deployed in the real-world will require the ability to reliably \textit{cooperate} with humans (as well as other, heterogeneous AI agents). To provi…
Strategically Efficient Exploration in Competitive Multi-agent Reinforcement Learning
Robert Loftin, Aadirupa Saha, Sam Devlin +1
High sample complexity remains a barrier to the application of reinforcement learning (RL), particularly in multi-agent systems. A large body of work has demonstrated that explorat…