Risk Averse Path Planning Using Lipschitz Approximated Wasserstein Distributionally Robust Deep Q-Learning
(2022)Department of Automatic Control
- Abstract
- We investigate the problem of risk averse robot path planning using the deep reinforcement learning and distributionally robust optimization perspectives. Our problem formulation involves modelling the robot as a stochastic linear dynamical system, assuming that a collection of process noise samples is available. We cast the risk averse motion planning problem as a Markov decision process and propose a continuous reward function design that explicitly takes into account the risk of collision with obstacles while encouraging the robot’s motion towards the goal. We learn the risk-averse robot control actions through Lipschitz approximated Wasserstein distributionally robust deep Q-Learning to hedge against the noise uncertainty. The learned... (More)
- We investigate the problem of risk averse robot path planning using the deep reinforcement learning and distributionally robust optimization perspectives. Our problem formulation involves modelling the robot as a stochastic linear dynamical system, assuming that a collection of process noise samples is available. We cast the risk averse motion planning problem as a Markov decision process and propose a continuous reward function design that explicitly takes into account the risk of collision with obstacles while encouraging the robot’s motion towards the goal. We learn the risk-averse robot control actions through Lipschitz approximated Wasserstein distributionally robust deep Q-Learning to hedge against the noise uncertainty. The learned control actions result in a safe and risk averse trajectory from the source to the goal, avoiding all the obstacles. Various supporting numerical simulations are presented to demonstrate our proposed approach. (Less)
Please use this url to cite or link to this publication:
http://lup.lub.lu.se/student-papers/record/9094536
- author
- Alptürk, Cem
- supervisor
- organization
- year
- 2022
- type
- H3 - Professional qualifications (4 Years - )
- subject
- report number
- TFRT-6166
- ISSN
- 0280-5316
- language
- English
- id
- 9094536
- date added to LUP
- 2022-08-12 09:56:33
- date last changed
- 2022-08-12 09:56:33
@misc{9094536,
abstract = {{We investigate the problem of risk averse robot path planning using the deep reinforcement learning and distributionally robust optimization perspectives. Our problem formulation involves modelling the robot as a stochastic linear dynamical system, assuming that a collection of process noise samples is available. We cast the risk averse motion planning problem as a Markov decision process and propose a continuous reward function design that explicitly takes into account the risk of collision with obstacles while encouraging the robot’s motion towards the goal. We learn the risk-averse robot control actions through Lipschitz approximated Wasserstein distributionally robust deep Q-Learning to hedge against the noise uncertainty. The learned control actions result in a safe and risk averse trajectory from the source to the goal, avoiding all the obstacles. Various supporting numerical simulations are presented to demonstrate our proposed approach.}},
author = {{Alptürk, Cem}},
issn = {{0280-5316}},
language = {{eng}},
note = {{Student Paper}},
title = {{Risk Averse Path Planning Using Lipschitz Approximated Wasserstein Distributionally Robust Deep Q-Learning}},
year = {{2022}},
}