We present a scalable and effective exploration strategy based on Thompson sampling for
reinforcement learning (RL). One of the key shortcomings of existing Thompson sampling …

We consider reinforcement learning in an environment modeled by an episodic, tabular,
step-dependent Markov decision process of horizon $ H $ with $ S $ states, and $ A …

We consider the problem of quantifying uncertainty over expected cumulative rewards in
model-based reinforcement learning. In particular, we focus on characterizing the variance …

In this paper, we introduce Randomized Q-learning (RandQL), a novel randomized model-
free algorithm for regret minimization in episodic Markov Decision Processes (MDPs). To the …

Despite remarkable successes, deep reinforcement learning algorithms remain sample
inefficient: they require an enormous amount of trial and error to find good policies. Model …

Abstract We propose two Thompson Sampling-like, model-based learning algorithms for
episodic Markov decision processes (MDPs) with a finite time horizon. Our proposed …

\textit {MinMaxMin} $ Q $-learning is a novel\textit {optimistic} Actor-Critic algorithm that
addresses the problem of\textit {overestimation} bias ($ Q $-estimations are overestimating …

In this paper we propose a general methodology to derive regret bounds for randomized
multi-armed bandit algorithms. It consists in checking a set of sufficient conditions on the …

В данной статье проведен обзор как исторических достижений, так и современных
результатов в области марковских процессов принятия решений (Markov Decision …

Deep reinforcement learning (DRL) has made significant strides in addressing tasks with
high-dimensional continuous action spaces. However, the field still faces the challenges of …