95 citations · 110 across the 5 of their papers we have counts for
5 papers
The AI Agent Index
Stephen Casper, Luke Bailey, Rosco Hunter +12
Leading AI developers and startups are increasingly deploying agentic AI systems that can plan and execute complex tasks with limited human involvement. However, there is currently…
Open Problems and Fundamental Limitations of Reinforcement Learning from Human Feedback
Stephen Casper, Xander Davies, Claudia Shi +29
Reinforcement learning from human feedback (RLHF) is a technique for training AI systems to align with human goals. RLHF has emerged as the central method used to finetune state-of…
Learning Symbolic Representations for Reinforcement Learning of Non-Markovian Behavior
Phillip J. K. Christoffersen, Andrew C. Li, Rodrigo Toro Icarte +1
Many real-world reinforcement learning (RL) problems necessitate learning complex, temporally extended behavior that may only receive reward signal when the behavior is completed.…
Formal Contracts Mitigate Social Dilemmas in Multi-Agent RL
Andreas A. Haupt, Phillip J. K. Christoffersen, Mehul Damani +1
Multi-agent Reinforcement Learning (MARL) is a powerful tool for training autonomous agents acting independently in a common environment. However, it can lead to sub-optimal behavi…
The act of remembering: a study in partially observable reinforcement learning
Rodrigo Toro Icarte, Richard Valenzano, Toryn Q. Klassen +3
Reinforcement Learning (RL) agents typically learn memoryless policies---policies that only consider the last observation when selecting actions. Learning memoryless policies is ef…