Accelerated Reinforcement Learning for Temporal Logic Control Objectives
Author:
Affiliation:
1. Washington University in St. Louis,Department of Electrical and Systems Engineering,St. Louis,MO,USA,63130
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/9981026/9981028/09981759.pdf?arnumber=9981759
Reference31 articles.
1. Computational Benefits of Intermediate Rewards for Goal-Reaching Policy Learning
2. Reward Machines: Exploiting Reward Function Structure in Reinforcement Learning
3. Integrated Architectures for Learning, Planning, and Reacting Based on Approximating Dynamic Programming
4. Overcoming exploration: Deep reinforcement learning in complex environments from temporal logic specifications;cai;ArXiv Preprint,2022
5. Discounted UCB1-tuned for Q-learning
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1. Exploiting Transformer in Sparse Reward Reinforcement Learning for Interpretable Temporal Logic Motion Planning;IEEE Robotics and Automation Letters;2023-08
2. Learning Minimally-Violating Continuous Control for Infeasible Linear Temporal Logic Specifications;2023 American Control Conference (ACC);2023-05-31
3. Verified Compositions of Neural Network Controllers for Temporal Logic Control Objectives;2022 IEEE 61st Conference on Decision and Control (CDC);2022-12-06
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