This repository is a curated list of resources for Deep Reinforcement Learning (DRL), aimed at researchers and practitioners seeking to understand and advance the field. It provides a comprehensive overview of foundational concepts, benchmark frameworks, and cutting-edge research across various DRL subfields, serving as a valuable knowledge base for AGI development.

How It Works

The project functions as an extensive, categorized bibliography of academic papers, code repositories, and benchmark environments related to Deep Reinforcement Learning. It organizes contributions by key DRL paradigms such as value-based methods, policy gradients, actor-critic architectures, model-based approaches, and specialized areas like multi-agent RL, meta-learning, and safety. This structured approach facilitates efficient navigation and discovery of relevant research.

Quick Start & Requirements

This is a curated list, not a runnable software package. No installation or execution commands are applicable.

Highlighted Details

• Comprehensive categorization of DRL research, covering over 50 sub-topics from foundational theory to advanced applications.
• Includes links to numerous academic papers (arXiv) and associated code repositories for practical implementation.
• Features benchmarks and frameworks like Brax, Spriteworld, Dopamine, and StarCraft II for empirical evaluation.
• Highlights recent advancements and emerging trends, with updates noted from 2017 through early 2024.

Maintenance & Community

The repository is maintained by tigerneil and appears to be community-driven, with updates indicated by dates. Specific community channels or active contributors beyond the primary maintainer are not detailed in the README.

Licensing & Compatibility

The repository itself, being a list of links, does not have a specific license. The linked resources (papers, code) are subject to their respective licenses, which vary. Compatibility for commercial use or closed-source linking depends entirely on the licenses of the individual linked projects.

Limitations & Caveats

As a curated list, the content's accuracy and completeness are dependent on the maintainer's and community's ongoing efforts. The rapid pace of DRL research means the list may not always reflect the absolute latest publications immediately.