5 papers
Gradient-based Stochastic Optimization of Utility-based Shortfall Risk
Sumedh Gupte, Prashanth L. A., Sanjay P. Bhat
We consider the problems of estimation and optimization of utility-based shortfall risk (UBSR). We extend UBSR to cover possibly unbounded random variables. We cover prominent risk…
Markov Chain Variance Estimation: A Stochastic Approximation Approach
Shubhada Agrawal, Prashanth L. A., Siva Theja Maguluri
We consider the problem of estimating the asymptotic variance of a function defined on a Markov chain, an important step for statistical inference of the stationary mean. We design…
Policy Gradient Methods for Distortion Risk Measures
Nithia Vijayan, Prashanth L. A
We propose policy gradient algorithms which learn risk-sensitive policies in a reinforcement learning (RL) framework. Our proposed algorithms maximize the distortion risk measure (…
Risk-Sensitive Reinforcement Learning via Policy Gradient Search
Prashanth L. A., Michael Fu
The objective in a traditional reinforcement learning (RL) problem is to find a policy that optimizes the expected value of a performance metric such as the infinite-horizon cumula…
Bandit algorithms to emulate human decision making using probabilistic distortions
Ravi Kumar Kolla, Prashanth L. A., Aditya Gopalan +3
Motivated by models of human decision making proposed to explain commonly observed deviations from conventional expected value preferences, we formulate two stochastic multi-armed…