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Found 12 papers, 3 papers with code
Incentivized Learning in Principal-Agent Bandit Games
This work considers a repeated principal-agent bandit game, where the principal can only interact with her environment through the agent.
Demonstration-Regularized RL
In particular, we study the demonstration-regularized reinforcement learning that leverages the expert demonstrations by KL-regularization for a policy learned by behavior cloning.
Finite-Sample Analysis of the Temporal Difference Learning
In this paper we consider the problem of obtaining sharp bounds for the performance of temporal difference (TD) methods with linear functional approximation for policy evaluation in discounted Markov Decision Processes.
Generative Flow Networks as Entropy-Regularized RL
We demonstrate how the task of learning a generative flow network can be efficiently redefined as an entropy-regularized RL problem with a specific reward and regularizer structure.
Sharp Deviations Bounds for Dirichlet Weighted Sums with Application to analysis of Bayesian algorithms
These bounds are based on a novel integral representation of the density of a weighted Dirichlet sum.
Orthogonal Directions Constrained Gradient Method: from non-linear equality constraints to Stiefel manifold
We consider the problem of minimizing a non-convex function over a smooth manifold $\mathcal{M}$.
Fast Rates for Maximum Entropy Exploration
Finally, we apply developed regularization techniques to reduce sample complexity of visitation entropy maximization to $\widetilde{\mathcal{O}}(H^2SA/\varepsilon^2)$, yielding a statistical separation between maximum entropy exploration and reward-free exploration.
Optimistic Posterior Sampling for Reinforcement Learning with Few Samples and Tight Guarantees
We consider reinforcement learning in an environment modeled by an episodic, finite, stage-dependent Markov decision process of horizon $H$ with $S$ states, and $A$ actions.
From Dirichlet to Rubin: Optimistic Exploration in RL without Bonuses
We propose the Bayes-UCBVI algorithm for reinforcement learning in tabular, stage-dependent, episodic Markov decision process: a natural extension of the Bayes-UCB algorithm by Kaufmann et al. (2012) for multi-armed bandits.
Primal-Dual Stochastic Mirror Descent for MDPs
We consider the problem of learning the optimal policy for infinite-horizon Markov decision processes (MDPs).
Improved Complexity Bounds in Wasserstein Barycenter Problem
In this paper, we focus on computational aspects of the Wasserstein barycenter problem.
Optimization and Control
Stochastic Saddle-Point Optimization for Wasserstein Barycenters
This leads to a complicated stochastic optimization problem where the objective is given as an expectation of a function given as a solution to a random optimization problem.
