MazeRL

MazeRL is a modular and flexible reinforcement learning (RL) framework designed to simplify the development, training, and evaluation of intelligent agents. It provides a high-level structure for building custom RL environments, algorithms, and training pipelines while maintaining full transparency and control. By combining flexibility with performance, MazeRL empowers researchers and developers to experiment, prototype, and deploy reinforcement learning solutions efficiently.

Features of MazeRL:

• Provides a modular architecture for building and customizing RL environments and algorithms.
• Supports distributed training and scalable experimentation across CPUs and GPUs.
• Offers built-in visualization, logging, and debugging tools for better model understanding.
• Integrates seamlessly with PyTorch and other popular deep learning frameworks.
• Includes benchmarking environments and utilities for reproducible research.
• Designed for extensibility, enabling rapid prototyping and deployment of new RL ideas.

To check the installed version of MazeRL, run the following commands:

$ sudo su
$ docker start practical_williams
$ docker ps
To access the web interface:
http://your-server-ip:8888

To check the access token:
$ docker logs practical_williams 2>&1 | grep -o 'token=[^ ]*' | tail -1


To check version:
$ docker exec -it practical_williams bash
$ pip show maze-rl

Disclaimer:

MazeRL is an open-source reinforcement learning framework developed and maintained by EnliteAI. It is intended for research and development purposes to accelerate RL experimentation and deployment. Users are responsible for ensuring proper configuration and usage in their projects. Always refer to the official MazeRL GitHub repository and documentation for the most accurate and updated information.