This repository is a comprehensive bibliography of academic papers related to Optimal Control, Reinforcement Learning, and Motion Planning. It serves as a curated resource for researchers, engineers, and students in these fields, providing a structured overview of key concepts, algorithms, and applications, particularly in autonomous driving.

How It Works

The bibliography is organized by topic, with sub-sections detailing specific algorithms, theories, and applications. Each entry includes the paper title, authors, and publication year, often with a brief annotation or link. The structure allows users to navigate through foundational concepts like Dynamic Programming and Reinforcement Learning theory, to advanced topics such as Model Predictive Control, Multi-Agent Reinforcement Learning, and Imitation Learning, with a strong emphasis on practical applications in autonomous driving.

Quick Start & Requirements

This is a static bibliography and does not require installation or execution. It is intended for informational purposes.

Highlighted Details

• Extensive coverage of Reinforcement Learning sub-fields including Value-based, Policy-based, Actor-critic, and Derivative-free methods.
• Detailed sections on Motion Planning, covering Search, Sampling, Optimization, and Reactive approaches.
• Significant focus on applications within Autonomous Driving, including Imitation Learning, Inverse Reinforcement Learning, and Motion Planning.
• Includes seminal papers and recent advancements across all listed topics.

Maintenance & Community

The repository appears to be a personal project by "eleurent" and does not indicate active community development or maintenance beyond its initial curation. There are no links to community forums, roadmaps, or contributor information provided.

Licensing & Compatibility

The repository itself does not specify a license. The content consists of references to academic papers, which are subject to their respective copyright and licensing terms.

Limitations & Caveats

This is a curated list of papers and does not provide code, implementations, or direct access to the research content itself. The breadth of topics covered means that some areas may be less detailed than others.