Epistemic Risk-Sensitive Reinforcement Learning

06/14/2019
by   Hannes Eriksson, et al.
0

We develop a framework for interacting with uncertain environments in reinforcement learning (RL) by leveraging preferences in the form of utility functions. We claim that there is value in considering different risk measures during learning. In this framework, the preference for risk can be tuned by variation of the parameter β and the resulting behavior can be risk-averse, risk-neutral or risk-taking depending on the parameter choice. We evaluate our framework for learning problems with model uncertainty. We measure and control for epistemic risk using dynamic programming (DP) and policy gradient-based algorithms. The risk-averse behavior is then compared with the behavior of the optimal risk-neutral policy in environments with epistemic risk.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
05/23/2019

Estimating Risk and Uncertainty in Deep Reinforcement Learning

This paper demonstrates a novel method for separately estimating aleator...
research
01/26/2023

On the Global Convergence of Risk-Averse Policy Gradient Methods with Dynamic Time-Consistent Risk Measures

Risk-sensitive reinforcement learning (RL) has become a popular tool to ...
research
03/02/2020

Risk-Averse Learning by Temporal Difference Methods

We consider reinforcement learning with performance evaluated by a dynam...
research
10/11/2022

On the homogeneity of measures for binary associations

Applied researchers often claim that the risk difference is more heterog...
research
12/26/2021

Reinforcement Learning with Dynamic Convex Risk Measures

We develop an approach for solving time-consistent risk-sensitive stocha...
research
08/23/2021

Robust Risk-Aware Reinforcement Learning

We present a reinforcement learning (RL) approach for robust optimisatio...
research
11/28/2017

Risk-sensitive Inverse Reinforcement Learning via Semi- and Non-Parametric Methods

The literature on Inverse Reinforcement Learning (IRL) typically assumes...

Please sign up or login with your details

Forgot password? Click here to reset