7 papers
Mini-Batch Risk-Averse Deep Q-Learning: A Robot Navigation Case Study
Aayush Patel, Andrzej Ruszczyński
We study the control of Markov decision processes in which the quality of a policy is evaluated by a dynamic, time-consistent Markov risk measure rather than by an expected discoun…
Reinforcement Learning with Markov Risk Measures and Multipattern Risk Approximation
Andrzej Ruszczynski, Tiangang Zhang
For a risk-averse finite-horizon Markov Decision Problem, we introduce a special class of Markov coherent risk measures, called mini-batch measures. We also define the class of mul…
Convergence Rate of a Functional Learning Method for Contextual Stochastic Optimization
Noel Smith, Andrzej Ruszczynski
We consider a stochastic optimization problem involving two random variables: a context variable and a dependent variable . The objective is to minimize the expected value o…
Federated Calculation of the Free-Support Transportation Barycenter by Single-Loop Dual Decomposition
Zhengqi Lin, Andrzej Ruszczyński
We propose an efficient federated dual decomposition algorithm for calculating the Wasserstein barycenter of several distributions, including choosing the support of the solution.…
A Functional Model Method for Nonconvex Nonsmooth Conditional Stochastic Optimization
Andrzej Ruszczyński, Shangzhe Yang
We consider stochastic optimization problems involving an expected value of a nonlinear function of a base random vector and a conditional expectation of another function depending…
Fast Dual Subgradient Optimization of the Integrated Transportation Distance Between Stochastic Kernels
Zhengqi Lin, Andrzej Ruszczynski
A generalization of the Wasserstein metric, the integrated transportation distance, establishes a novel distance between probability kernels of Markov systems. This metric serves a…