Provably Safe Reinforcement Learning via Action Projection Using Reachability Analysis and Polynomial Zonotopes
Author:
Affiliation:
1. Department of Computer Engineering, Technical University of Munich, Garching, Germany
2. Department of Computer Science, Stony Brook University, Stony Brook, NY, USA
Funder
European Research Council
Project justITSELF
German Research Foundation through the Research Training Group ConVeY
Air Force Office of Scientific Research and the Office of Naval Research
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Link
http://xplorestaging.ieee.org/ielx7/9552933/9973428/10068193.pdf?arnumber=10068193
Reference61 articles.
1. A Closer Look at Invalid Action Masking in Policy Gradient Algorithms
2. Extensions of polynomial zonotopes and their application to verification of cyber-physical systems;kochdumper,2022
3. Safe Reinforcement Learning on Autonomous Vehicles
4. Computationally Efficient Fail-safe Trajectory Planning for Self-driving Vehicles Using Convex Optimization
5. Trial without error: Towards safe reinforcement learning via human intervention;saunders;Proc 1st Int Conf Autonomous Agents Multiagent Syst,0
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3. Fear-Neuro-Inspired Reinforcement Learning for Safe Autonomous Driving;IEEE Transactions on Pattern Analysis and Machine Intelligence;2023
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