GitHub - upb-lea/reinforcement_learning_course_materials: Lecture notes, tutoria...
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Reinforcement Learning Course Materials
Lecture notes, tutorial tasks including solutions as well as online videos for the reinforcement learning course hosted by Paderborn University. Source code for the entire course material is open and everyone is cordially invited to use it for self-learning (students) or to set up your own course (lecturers).
Lecture Content
Introduction to Reinforcement Learning
Markov Decision Processes
Dynamic Programming
Monte Carlo Methods
Temporal-Difference Learning
n-Step Bootstrapping
Planning and Learning with Tabular Methods
Function Approximation with Supervised Learning
On-Policy Prediction with Function Approximation
Value-Based Control with Function Approximation
Eligibility Traces
Policy Gradient Methods
Further Contemporary RL Algorithms (DDPG, TD3, TRPO, PPO)
Summary of Part One: Reinforcement Learning in Finite State and Action Spaces
Summary of Part Two: Course Completion and Outlook
- Full course slides
Exercise Content
Basics of Python for Scientific Computing
Manually Solving Basic Markov Chain, Reward and Decision Problems
The Beer-Bachelor and Dynamic Programming (the Shortest Beer Problem)
Drive Through the Race Track with Monte Carlo Learning
Drive even Faster Using Temporal-Difference Learning
Stabilizing the Inverted Pendulum by Tabular n-Step Methods
Boosting the Inverted Pendulum by Integrating Learning & Planning (Dyna Framework)
Predicting the Operating Behavior of a Real Electric Drive Systems with Supervised Learning
Evaluate the Performance of Given Agents in the Mountain Car Problem Using Function Approximation
Escape from the Mountain Car Valley Using Semi-Gradient Sarsa & Least Square Policy Iteration
Improve the Value-Based Mount Car Solution using Sarsa(Lambda)
Landing on the Moon with REINFORCE and Actor-Critic Methods
Shoot to the moon with DDPG & PPO
Citation
Please use the following BibTeX entry for citing us:
@Misc{KSWW2020,
author = {Wilhelm Kirchgässner and Maximilian Schenke and Oliver Wallscheid and Daniel Weber},
note = {Paderborn University},
title = {Reinforcement Learning Course Material},
year = {2020},
url = {https://github.com/upb-lea/reinforcement_learning_course_materials},
}
Contributions
We highly appreciate any feedback and input to the course material e.g.
- typos or content-related discussions (please raise an issue)
- adding new contents (please provide a pull request)
If you like to contribute to the repo to a larger extent, please do not hesitate to contact us directly.
Credits
The lecture notes are inspired by
The tutorials are partly using pre-packed environments from
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