A Probabilistic Perspective on Risk-sensitive Reinforcement Learning-Reference-Cited by-同舟云学术

A Probabilistic Perspective on Risk-sensitive Reinforcement Learning

Published:2022-06-08 Issue: Volume: Page:
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Container-title:2022 American Control Conference (ACC)
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Author:

Noorani Erfaun¹,Baras John S.¹

Affiliation:

1. University of Maryland College Park,Institute for System Research (ISR),Department of Electrical and Computer Engineering,College Park,MD,USA

Funder

Arm

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/9866948/9867142/09867288.pdf?arnumber=9867288

Reference27 articles.

1. Policy Gradients for CVaR-Constrained MDPs

2. Risk-Sensitive and Efficient Reinforcement Learning Algorithms;tamar,2015

3. R2PG: Risk-Sensitive and Reliable Policy Gradient;liu;The Workshops of the The Thirty-Second AAAI Conference on Artificial Intelligence New Orleans Louisiana USA February 2-7 2018,2018

4. Entropic Risk Measure in Policy Search

5. Optimal stochastic linear systems with exponential performance criteria and their relation to deterministic differential games

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2. A Probabilistic Treatment of (PO)MDPs with Multiplicative Reward Structure;2024 European Control Conference (ECC);2024-06-25

3. Risk-Sensitive RL Using Sampling-Based Expectation-Maximization;2023 62nd IEEE Conference on Decision and Control (CDC);2023-12-13

4. Parameterized input inference for approximate stochastic optimal control;2023 American Control Conference (ACC);2023-05-31