2 citations · 2 across the 8 of their papers we have counts for
8 papers
Efficient Exploration Is Enough
Mikel Malagón, Jon Vadillo, Josu Ceberio +2
This work introduces an alternative view of efficient exploration and studies its theoretical and empirical implications in the absence of extrinsic rewards. Specifically, we defin…
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…
KETCHUP: K-Step Return Estimation for Sequential Knowledge Distillation
Jiabin Fan, Guoqing Luo, Michael Bowling +1
We propose a novel k-step return estimation method (called KETCHUP) for Reinforcement Learning(RL)-based knowledge distillation (KD) in text generation tasks. Our idea is to induce…
Model-Based Exploration in Monitored Markov Decision Processes
Alireza Kazemipour, Simone Parisi, Matthew E. Taylor +1
A tenet of reinforcement learning is that the agent always observes rewards. However, this is not true in many realistic settings, e.g., a human observer may not always be availabl…
Beyond Optimism: Exploration With Partially Observable Rewards
Simone Parisi, Alireza Kazemipour, Michael Bowling
Exploration in reinforcement learning (RL) remains an open challenge. RL algorithms rely on observing rewards to train the agent, and if informative rewards are sparse the agent le…
Monitored Markov Decision Processes
Simone Parisi, Montaser Mohammedalamen, Alireza Kazemipour +2
In reinforcement learning (RL), an agent learns to perform a task by interacting with an environment and receiving feedback (a numerical reward) for its actions. However, the assum…
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…