Thompson sampling has proven effective across a wide range of stationary bandit
environments. However, as we demonstrate in this paper, it can perform poorly when …

A digital twin (DT) leverages a virtual representation of the physical world, along with
communication (eg, 6G), computing (eg, edge computing), and artificial intelligence (AI) …

All biological and artificial agents must act given limits on their ability to acquire and process
information. As such, a general theory of adaptive behavior should be able to account for the …

Abstract Information-directed sampling (IDS) has recently demonstrated its potential as a
data-efficient reinforcement learning algorithm. However, it is still unclear what is the right …

The quintessential model-based reinforcement-learning agent iteratively refines its
estimates or prior beliefs about the true underlying model of the environment. Recent …

A default assumption in the design of reinforcement-learning algorithms is that a decision-
making agent always explores to learn optimal behavior. In sufficiently complex …

This work designs and analyzes a novel set of algorithms for multi-agent reinforcement
learning (MARL) based on the principle of information-directed sampling (IDS). These …

Throughout the cognitive-science literature, there is widespread agreement that decision-
making agents operating in the real world do so under limited information-processing …

A sequential decision-making agent balances between exploring to gain new knowledge
about an environment and exploiting current knowledge to maximize immediate reward. For …

Bayesian optimization (BO) is widely used for black-box optimization problems, and have
been shown to perform well in various real-world tasks. However, most of the existing BO …