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Access the book Reinforcement Learning for Sequential Decision and Optimal Control at https://link.springer.com/book/10.1007/978-981-19-7784-8.
Provide a comprehensive and thorough introduction to reinforcement learning, ranging from theory to algorithm
Introduce reinforcement learning from both artificial intelligence and optimal control perspectives
Written by a respected expert in the interdisciplinary field of industrial control and artificial intelligence
Make sure to also check out GOPS (General Optimal control Problem Solver), a comprehensive Reinforcement Learning toolchain to cover main links in the whole industrial control process, including control problem modeling, policy network training, offline simulation verification, and controller code deployment.
Setup conda first, and install dependencies.
conda env create -n rlbook -f environment.yml
conda activate rlbookThen open each folder and run main or plot Python script.
Chap_3_4_CleanRobot: Code for robot cleaning example in Chapter 3 and 4.Chap_5_AutoCar_GridRoad: Code for autonomous car example in Chapter 5.Chap_6_Actor_Critic_Algorithm: Code for actor-critic algorithm in Chapter 6.Chap_7_AC_wtih_Baseline: Code for AC algorithm with baseline comparison in Chapter 7.Chap_8_Veh_Track_Ctrl: Code for vehicle tracking control example in Chapter 8.Chap_9_Car_Brake_Control: Code for emergency braking control example in Chapter 9.
If you find this code useful in your research, please consider citing:
S Eben Li. Reinforcement Learning for Sequential Decision and Optimal Control. Springer Verlag, Singapore, 2023.
Or in BibTeX style:
@book{li2023reinforcement,
title={Reinforcement Learning for Sequential Decision and Optimal Control},
author={Li, Shengbo Eben},
year={2023},
publisher={Springer Verlag, Singapore}
}