2 citations · 2 across the 8 of their papers we have counts for
4 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…
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…