In toxicology research, experiments are often conducted to determine the effect of toxicant
exposure on the behavior of mice, where mice are randomized to receive the toxicant or not …

Existing statistical methods can be used to estimate a policy, or a mapping from covariates to
decisions, which can then instruct decision makers (eg, whether to administer hypotension …

How do animals learn? This remains an elusive question in neuroscience. Whereas
reinforcement learning often focuses on the design of algorithms that enable artificial agents …

In reinforcement learning from human feedback, it is common to optimize against a reward
model trained to predict human preferences. Because the reward model is an imperfect …

Reinforcement learning theories postulate that actions are chosen to maximize a long-term
sum of positive outcomes based on value functions, which are subjective estimates of future …

Reinforcement learning (RL) is a theoretical framework that describes how agents learn to
select options that maximize rewards and minimize punishments over time. We often make …

Reinforcement learning is a theoretical framework that describes how agents learn to select
options that maximize rewards and minimize punishments over time. We often make …

Reinforcement learning models have long promised to unify computational, psychological
and neural accounts of appetitively conditioned behavior. However, the bulk of data on …

Animals and humans often have to choose between options with reward distributions that
are initially unknown and can only be learned through experience. Recent experimental and …

Abstract Markov Decision Processes are the standard model of sequential decision-making
problems in reinforcement learning. However, as noted by Abel et al.[1], for some …