@inproceedings{6c78cd518edc4f8b837f8be4487a9eeb,
title = "Adaptive Risk Sensitive Path Integral for Model Predictive Control via Reinforcement Learning",
abstract = "We propose a reinforcement learning framework where an agent uses an internal nominal model for stochastic model predictive control (MPC) while compensating for a disturbance. Our work builds on the existing risk-aware optimal control with stochastic differential equations (SDEs) that aims to deal with such disturbance. However, the risk sensitivity and the noise strength of the nominal SDE in the riskaware optimal control are often heuristically chosen. In the proposed framework, the risk-taking policy determines the behavior of the MPC to be risk-seeking (exploration) or risk-averse (exploitation). Specifically, we employ the risk-aware path integral control that can be implemented as a Monte-Carlo (MC) sampling with fast parallel simulations using a GPU. The MC sampling implementations of the MPC have been successful in robotic applications due to their real-time computation capability. The proposed framework that adapts the noise model and the risk sensitivity outperforms the standard model predictive path integral in simulation environments that have disturbances.",
author = "Yoon, {Hyung Jin} and Chuyuan Tao and Hunmin Kim and Naira Hovakimyan and Voulgaris, {Petros G}",
note = "Funding Information: This research is supported by NSF CPS #1932529, NSF CMMI #1663460, NSF CMMI #2137753, and UNR internal funding. Publisher Copyright: {\textcopyright} 2023 IEEE.; 31st Mediterranean Conference on Control and Automation, MED 2023 ; Conference date: 26-06-2023 Through 29-06-2023",
year = "2023",
doi = "10.1109/MED59994.2023.10185876",
language = "English (US)",
series = "2023 31st Mediterranean Conference on Control and Automation, MED 2023",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "926--931",
booktitle = "2023 31st Mediterranean Conference on Control and Automation, MED 2023",
address = "United States",
}