4 papers · 1 filter
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…
Fast gradient descent for drifting least squares regression, with application to bandits
Nathaniel Korda, Prashanth L. A., Rémi Munos
Online learning algorithms require to often recompute least squares regression estimates of parameters. We study improving the computational complexity of such algorithms by using…