This repository provides a comprehensive platform for Deep Reinforcement Learning (DRL), aiming to make DRL accessible for researchers, learners, and enthusiasts. It covers foundational concepts, cutting-edge algorithms, and practical applications, drawing inspiration from landmark achievements like AlphaGo.

How It Works

The project leverages the synergy between Deep Learning (DL) and Reinforcement Learning (RL) to tackle complex problems. By using neural networks to approximate value functions, DRL enables agents to learn optimal policies directly from high-dimensional sensory inputs, as demonstrated in applications ranging from game playing to autonomous systems.

Quick Start & Requirements

• Installation and usage details are not explicitly provided in the README.
• The project appears to be a knowledge hub and resource collection rather than a runnable code library.

Highlighted Details

• Detailed historical timeline of DRL advancements, including AlphaGo's milestones.
• Extensive list of DRL application areas, such as autonomous driving, robotics, NLP, and finance.
• Compilation of leading research institutions and key figures in the DRL field.
• Guidance on learning DRL, including recommended resources and open-source frameworks.

Maintenance & Community

The project is maintained by the "DeepRL-Lab" community, with contributors from academia (e.g., Tsinghua, Peking University, Oxford) and industry (e.g., Tencent, Alibaba, Huawei). A WeChat group is available for community interaction.

Licensing & Compatibility

The repository's licensing is not specified in the README.

Limitations & Caveats

The README focuses on educational and resource-gathering aspects of DRL, and does not provide executable code or setup instructions, making it unsuitable for direct implementation without further development.