The most influential account of phasic dopamine holds that it reports reward prediction
errors (RPEs). The RPE-based interpretation of dopamine signaling is, in its original form …

We consider the batch (off-line) policy learning problem in the infinite horizon Markov
Decision Process. Motivated by mobile health applications, we focus on learning a policy …

Whittle index policy is a heuristic to the intractable restless multi-armed bandits (RMAB)
problem. Although it is provably asymptotically optimal, finding Whittle indices remains …

We consider experiments in dynamical systems where interventions on some experimental
units impact other units through a limiting constraint (such as a limited supply of products) …

The deadly triad refers to the instability of a reinforcement learning algorithm when it
employs off-policy learning, function approximation, and bootstrapping simultaneously. In …

We develop theory and algorithms for average-reward on-policy Reinforcement Learning
(RL). We first consider bounding the difference of the long-term average reward for two …

We consider off-policy policy evaluation with function approximation (FA) in average-reward
MDPs, where the goal is to estimate both the reward rate and the differential value function …

The focus of this paper is on sample complexity guarantees of average-reward
reinforcement learning algorithms, which are known to be more challenging to study than …

The main challenge of multiagent reinforcement learning is the difficulty of learning useful
policies in the presence of other simultaneously learning agents whose changing behaviors …

As a framework for sequential decision-making, Reinforcement Learning (RL) has been
regarded as an essential component leading to Artificial General Intelligence (AGI) …