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Found 13 papers, 2 papers with code
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.
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.
Theoretical guarantees for neural control variates in MCMC
In this paper, we propose a variance reduction approach for Markov chains based on additive control variates and the minimization of an appropriate estimate for the asymptotic variance.
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.
Primal-dual regression approach for Markov decision processes with general state and action space
As a result, our method allows for the construction of tight upper and lower biased approximations of the value functions, and, provides tight approximations to the optimal policy.
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.
Variance Reduction for Policy-Gradient Methods via Empirical Variance Minimization
Policy-gradient methods in Reinforcement Learning(RL) are very universal and widely applied in practice but their performance suffers from the high variance of the gradient estimate.
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.
A Reproducing Kernel Hilbert Space approach to singular local stochastic volatility McKean-Vlasov models
Motivated by the challenges related to the calibration of financial models, we consider the problem of numerically solving a singular McKean-Vlasov equation $$ d X_t= \sigma(t, X_t) X_t \frac{\sqrt v_t}{\sqrt {E[v_t|X_t]}}dW_t, $$ where $W$ is a Brownian motion and $v$ is an adapted diffusion process.
From optimal martingales to randomized dual optimal stopping
As a main feature, in a possibly large family of optimal martingales the algorithm efficiently selects a martingale that is as close as possible to the Doob martingale.
Probability Optimization and Control Computational Finance 91G60, 65C05, 60G40
Rates of convergence for density estimation with generative adversarial networks
In this work we undertake a thorough study of the non-asymptotic properties of the vanilla generative adversarial networks (GANs).
Reinforced optimal control
Least squares Monte Carlo methods are a popular numerical approximation method for solving stochastic control problems.
Optimal stopping via reinforced regression
In this note we propose a new approach towards solving numerically optimal stopping problems via reinforced regression based Monte Carlo algorithms.
