2 citations · 2 across the 8 of their papers we have counts for
3 papers · 1 filter
Toward Agents That Reason About Their Computation
Adrian Orenstein, Jiajing Chen, Jessica Chen +4
While reinforcement learning agents can achieve superhuman performance in many complex tasks, they typically do not become more computationally efficient as they improve. In contra…
Generalization in Monitored Markov Decision Processes (Mon-MDPs)
Montaser Mohammedalamen, Michael Bowling
Reinforcement learning (RL) typically models the interaction between the agent and environment as a Markov decision process (MDP), where the rewards that guide the agent's behavior…
Assessing the Interpretability of Programmatic Policies with Large Language Models
Zahra Bashir, Michael Bowling, Levi H. S. Lelis
Although the synthesis of programs encoding policies often carries the promise of interpretability, systematic evaluations were never performed to assess the interpretability of th…