2 citations · 3 across the 5 of their papers we have counts for
5 papers
Decoupling Time and Risk: Risk-Sensitive Reinforcement Learning with General Discounting
Mehrdad Moghimi, Anthony Coache, Hyejin Ku
Distributional reinforcement learning (RL) is a powerful framework increasingly adopted in safety-critical domains for its ability to optimize risk-sensitive objectives. However, t…
Robust Reinforcement Learning with Dynamic Distortion Risk Measures
Anthony Coache, Sebastian Jaimungal
In a reinforcement learning (RL) setting, the agent's optimal strategy heavily depends on her risk preferences and the underlying model dynamics of the training environment. These…
Eliciting Risk Aversion with Inverse Reinforcement Learning via Interactive Questioning
Ziteng Cheng, Anthony Coache, Sebastian Jaimungal
We investigate a framework for robo-advisors to estimate non-expert clients' risk aversion using adaptive binary-choice questionnaires. We model risk aversion using cost functions…
Conditionally Elicitable Dynamic Risk Measures for Deep Reinforcement Learning
Anthony Coache, Sebastian Jaimungal, Álvaro Cartea
We propose a novel framework to solve risk-sensitive reinforcement learning (RL) problems where the agent optimises time-consistent dynamic spectral risk measures. Based on the not…
Reinforcement Learning with Dynamic Convex Risk Measures
Anthony Coache, Sebastian Jaimungal
We develop an approach for solving time-consistent risk-sensitive stochastic optimization problems using model-free reinforcement learning (RL). Specifically, we assume agents asse…