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Found 8 papers, 1 papers with code
Risk Estimation in a Markov Cost Process: Lower and Upper Bounds
To the best of our knowledge, our work is the first to provide lower and upper bounds for estimating any risk measure beyond the mean within a Markovian setting.
Generalized Simultaneous Perturbation-based Gradient Search with Reduced Estimator Bias
We first present in detail unbalanced generalized simultaneous perturbation stochastic approximation (GSPSA) estimators and later present the balanced versions (B-GSPSA) of these.
Adaptive Estimation of Random Vectors with Bandit Feedback: A mean-squared error viewpoint
We consider the problem of sequentially learning to estimate, in the mean squared error (MSE) sense, a Gaussian $K$-vector of unknown covariance by observing only $m < K$ of its entries in each round.
Online Estimation and Optimization of Utility-Based Shortfall Risk
We derive non-asymptotic bounds on the estimation error in the number of samples.
On TD(0) with function approximation: Concentration bounds and a centered variant with exponential convergence
Furthermore, we propose a variant of TD(0) with linear approximators that incorporates a centering sequence, and establish that it exhibits an exponential rate of convergence in expectation.
Simultaneous Perturbation Algorithms for Batch Off-Policy Search
We propose novel policy search algorithms in the context of off-policy, batch mode reinforcement learning (RL) with continuous state and action spaces.
Actor-Critic Algorithms for Learning Nash Equilibria in N-player General-Sum Games
We then provide a characterization of solution points of these sub-problems that correspond to Nash equilibria of the underlying game and for this purpose, we derive a set of necessary and sufficient SG-SP (Stochastic Game - Sub-Problem) conditions.
Concentration bounds for temporal difference learning with linear function approximation: The case of batch data and uniform sampling
We propose a stochastic approximation (SA) based method with randomization of samples for policy evaluation using the least squares temporal difference (LSTD) algorithm.
