The rapid changes in the finance industry due to the increasing amount of data have
revolutionized the techniques on data processing and data analysis and brought new …

Thompson sampling is an algorithm for online decision problems where actions are taken
sequentially in a manner that must balance between exploiting what is known to maximize …

Sequential decision making, commonly formalized as Markov Decision Process (MDP)
optimization, is an important challenge in artificial intelligence. Two key approaches to this …

Decision-making in the face of uncertainty is a significant challenge in machine learning,
and the multi-armed bandit model is a commonly used framework to address it. This …

Bayesian methods for machine learning have been widely investigated, yielding principled
methods for incorporating prior information into inference algorithms. In this survey, we …

We study the use of randomized value functions to guide deep exploration in reinforcement
learning. This offers an elegant means for synthesizing statistically and computationally …

Computational results demonstrate that posterior sampling for reinforcement learning
(PSRL) dramatically outperforms existing algorithms driven by optimism, such as UCRL2 …

We derive an alternative proof for the regret of Thompson sampling (TS) in the stochastic
linear bandit setting. While we obtain a regret bound of order $ O (d^ 3/2\sqrtT) $ as in …

We propose randomized least-squares value iteration (RLSVI)–a new reinforcement
learning algorithm designed to explore and generalize efficiently via linearly parameterized …

We consider the exploration-exploitation dilemma in finite-horizon reinforcement learning
(RL). When the state space is large or continuous, traditional tabular approaches are …