6 citations · 17 across the 29 of their papers we have counts for
7 papers · 1 filter
Model-free Posterior Sampling via Learning Rate Randomization
Daniil Tiapkin, Denis Belomestny, Daniele Calandriello +6
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 bes…
Demonstration-Regularized RL
Daniil Tiapkin, Denis Belomestny, Daniele Calandriello +5
Incorporating expert demonstrations has empirically helped to improve the sample efficiency of reinforcement learning (RL). This paper quantifies theoretically to what extent this…
Improved High-Probability Bounds for the Temporal Difference Learning Algorithm via Exponential Stability
Sergey Samsonov, Daniil Tiapkin, Alexey Naumov +1
In this paper we consider the problem of obtaining sharp bounds for the performance of temporal difference (TD) methods with linear function approximation for policy evaluation in…
Generative Flow Networks as Entropy-Regularized RL
Daniil Tiapkin, Nikita Morozov, Alexey Naumov +1
The recently proposed generative flow networks (GFlowNets) are a method of training a policy to sample compositional discrete objects with probabilities proportional to a given rew…
Sharp Deviations Bounds for Dirichlet Weighted Sums with Application to analysis of Bayesian algorithms
Denis Belomestny, Pierre Menard, Alexey Naumov +2
In this work, we derive sharp non-asymptotic deviation bounds for weighted sums of Dirichlet random variables. These bounds are based on a novel integral representation of the dens…
Orthogonal Directions Constrained Gradient Method: from non-linear equality constraints to Stiefel manifold
Sholom Schechtman, Daniil Tiapkin, Michael Muehlebach +1
We consider the problem of minimizing a non-convex function over a smooth manifold . We propose a novel algorithm, the Orthogonal Directions Constrained Gradient Metho…
Fast Rates for Maximum Entropy Exploration
Daniil Tiapkin, Denis Belomestny, Daniele Calandriello +7
We address the challenge of exploration in reinforcement learning (RL) when the agent operates in an unknown environment with sparse or no rewards. In this work, we study the maxim…