LlamaGym is an open-source Python library that provides a unified interface for various AI models, including language models, computer vision models, and reinforcement learning models, allowing users to easily switch between different models and tasks, and enabling rapid prototyping and research. It supports popular models such...
Supports various types of environments, including Atari and MuJoCo simulations.
Provides a simple and intuitive API for interacting with environments.
Offers a wide range of customizable environment configurations and settings.
Includes a collection of pre-built environments for rapid development and prototyping.
Allows for seamless integration with popular reinforcement learning frameworks.
Supports multiple observation and action spaces, including discrete and continuous types.
Provides efficient and scalable environment implementations for high-performance computing.
Enables flexible and modular environment design for visualization and analysis.
What is LlamaGym?
LlamaGym is an open-source Python library that provides a unified interface for various reinforcement learning environments, allowing users to easily switch between different environments and compare their performance.
What environments are supported?
LlamaGym supports a wide range of environments, including classic control problems like CartPole and Acrobot, as well as more complex environments like MuJoCo and Robotics.
How do I install it?
You can install LlamaGym via pip by running the command `pip install llmagym` in your terminal, and it will automatically install all the required dependencies.
Is it compatible with Linux?
Yes, LlamaGym is compatible with Linux and has been tested on Ubuntu 18.04 and 20.04, and it should work on other Linux distributions as well.
Can I use it for research?
Yes, LlamaGym is suitable for research purposes, and its modular design allows for easy extension and customization of environments and agents.
Is it actively maintained?
Yes, LlamaGym is actively maintained, and the developers are open to contributions, bug reports, and feature requests from the community.
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