Introduction to Awesome Model-Based Reinforcement Learning
Overview
The "Awesome Model-Based Reinforcement Learning" project is a carefully curated collection of research papers focusing on model-based reinforcement learning (MBRL). This repository is a treasure trove for anyone interested in the field, offering a consolidated view of the cutting-edge advancements and methodologies in model-based RL. Regular updates are made to ensure it remains aligned with the forefront of research and development.
Goals and Highlights
The primary aim of this project is to gather and organize significant contributions to the field of model-based reinforcement learning. It serves as a one-stop reference for scholars, practitioners, and enthusiasts who wish to understand the progression and diversification of MBRL algorithms and theories.
The project continuously updates with lists from prestigious conferences like NeurIPS and ICML, allowing users to access recent breakthroughs and explore new directions in model-based RL research.
A Taxonomy of Model-Based RL Algorithms
Understanding the complex landscape of model-based RL algorithms can be challenging due to their modular and multifaceted nature. To address this, the project categorizes these algorithms into two primary groups:
- Learn the Model: Focuses on constructing models that imitate the environment.
- Given the Model: Deals with the effective utilization of pre-learned models to make decisions and predictions.
This categorization helps streamline the understanding of how different algorithms function and interact in model-based RL.
Papers
In its comprehensive collection, the project lists numerous research papers categorized by year and conference, such as NeurIPS, ICML, and ICLR. This collection includes classic papers that have significantly influenced the field and new contributions that push the boundaries of current research. Providing specifics like the paper title, authors, key problems addressed, and experimental environments aids in rapid comprehension and further exploration.
Tutorials and Resources
To support learning and development, the repository includes tutorials that help users grasp the underlying principles and methodologies of model-based RL. Additionally, a codebase is available for practical engagement, allowing users to experiment with and implement various algorithms outlined in the papers.
Contributing
The project welcomes contributions from the community, encouraging collaboration and knowledge sharing to expand and refine the collection further. Community support ensures the resource remains dynamic and incorporates diverse perspectives and insights.
License
The collection is maintained under a clear licensing framework, ensuring it respects intellectual property rights and encourages open access to information.
Overall, the "Awesome Model-Based Reinforcement Learning" project is an invaluable resource, facilitating learning, innovation, and collaboration among those passionate about advancing Artificial Intelligence and Reinforcement Learning.