104 citations · 140 across the 15 of their papers we have counts for
15 papers
Evaluating Criterion-Conditioned Behaviour of Large Language Models in Content Moderation
Danting Zhang, Bei Peng, Robert Loftin
Large language models (LLMs) demonstrate strong performance on standard content moderation benchmarks. However, these benchmarks often aggregate multiple moderation criteria into a…
ConventionPlay: Capability-Limited Training for Robust Ad-Hoc Collaboration
Abhishek Sriraman, Eleni Vasilaki, Robert Loftin
Ad-hoc collaboration often relies on identifying and adhering to shared conventions. However, when partners can follow multiple conventions, agents must do more than simply adapt;…
Counterfactual Conditional Likelihood Rewards for Multiagent Exploration
Ayhan Alp Aydeniz, Robert Loftin, Kagan Tumer
Efficient exploration is critical for multiagent systems to discover coordinated strategies, particularly in open-ended domains such as search and rescue or planetary surveying. Ho…
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…
Social Cooperation in Conversational AI Agents
Mustafa Mert Çelikok, Saptarashmi Bandyopadhyay, Robert Loftin
The development of AI agents based on large, open-domain language models (LLMs) has paved the way for the development of general-purpose AI assistants that can support human in tas…
Safe Multiagent Coordination via Entropic Exploration
Ayhan Alp Aydeniz, Enrico Marchesini, Robert Loftin +2
Many real-world multiagent learning problems involve safety concerns. In these setups, typical safe reinforcement learning algorithms constrain agents' behavior, limiting explorati…
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…
AI4GCC-Team -- Below Sea Level: Score and Real World Relevance
Phillip Wozny, Bram Renting, Robert Loftin +2
As our submission for track three of the AI for Global Climate Cooperation (AI4GCC) competition, we propose a negotiation protocol for use in the RICE-N climate-economic simulation…
Towards a Unifying Model of Rationality in Multiagent Systems
Robert Loftin, Mustafa Mert Çelikok, Frans A. Oliehoek
Multiagent systems deployed in the real world need to cooperate with other agents (including humans) nearly as effectively as these agents cooperate with one another. To design suc…
Uncoupled Learning of Differential Stackelberg Equilibria with Commitments
Robert Loftin, Mustafa Mert Çelikok, Herke van Hoof +2
In multi-agent problems requiring a high degree of cooperation, success often depends on the ability of the agents to adapt to each other's behavior. A natural solution concept in…
On the Impossibility of Learning to Cooperate with Adaptive Partner Strategies in Repeated Games
Robert Loftin, Frans A. Oliehoek
Learning to cooperate with other agents is challenging when those agents also possess the ability to adapt to our own behavior. Practical and theoretical approaches to learning in…
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…
Better Exploration with Optimistic Actor-Critic
Kamil Ciosek, Quan Vuong, Robert Loftin +1
Actor-critic methods, a type of model-free Reinforcement Learning, have been successfully applied to challenging tasks in continuous control, often achieving state-of-the art perfo…
Interactive Learning of Environment Dynamics for Sequential Tasks
Robert Loftin, Bei Peng, Matthew E. Taylor +2
In order for robots and other artificial agents to efficiently learn to perform useful tasks defined by an end user, they must understand not only the goals of those tasks, but als…
Interactive Learning from Policy-Dependent Human Feedback
James MacGlashan, Mark K Ho, Robert Loftin +5
This paper investigates the problem of interactively learning behaviors communicated by a human teacher using positive and negative feedback. Much previous work on this problem has…