MLGym
M
Mlgym
Overview :
MLGym is an open-source framework and benchmark developed by Meta's GenAI team and the UCSB NLP team for training and evaluating AI research agents. By offering diverse AI research tasks, it fosters the development of reinforcement learning algorithms and helps researchers train and evaluate models in real-world research scenarios. The framework supports various tasks, including computer vision, natural language processing, and reinforcement learning, aiming to provide a standardized testing platform for AI research.
Target Users :
MLGym is primarily designed for AI researchers and developers, especially those focusing on reinforcement learning, natural language processing, and computer vision. It provides a standardized platform for training and evaluating AI research agents, enabling researchers to validate new ideas and algorithms in real-world research scenarios.
Total Visits: 474.6M
Top Region: US(19.34%)
Website Views : 52.4K
Use Cases
Researchers can use MLGym to train AI agents to solve complex decision-making problems, such as finding optimal strategies in game theory tasks.
Through MLGym's trajectory visualization tool, researchers can intuitively analyze the behavior of AI agents and optimize model performance.
Utilizing MLGym's diverse tasks, researchers can evaluate the generalization ability of AI agents across different domains.
Features
Provides 13 diverse AI research tasks, covering multiple fields such as computer vision and natural language processing.
Supports the training and evaluation of reinforcement learning algorithms, helping researchers develop more efficient AI models.
Offers a trajectory visualization tool to facilitate researchers in analyzing and debugging model behavior.
Supports Docker and Podman containerized runtime environments to ensure experiment reproducibility and isolation.
Provides detailed installation and usage instructions to help researchers get started quickly.
How to Use
1. Clone the MLGym repository and install dependencies: `git clone https://github.com/facebookresearch/MLGym.git`, then install Python dependencies.
2. Create a `.env` file to configure environment variables, including API keys and paths.
3. Install Docker or Podman, and pull the container image: `docker pull aigym/mlgym-agent:latest`.
4. Use the `run.py` script to launch tasks, specifying the task configuration file, model, and container type.
5. Use `streamlit` to run the trajectory visualization tool and analyze the AI agent's behavior trajectories.
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