Welcome to the frp_rl repository! This project provides the source code for reproducing free random projection in the context of reinforcement learning. The repository focuses on integrating concepts from free probability, in-context reinforcement learning, and machine learning frameworks such as JAX.
Free random projection is a powerful technique that leverages concepts from free probability theory and random matrices. In the realm of reinforcement learning, this method can enhance the performance of various algorithms by providing more efficient state representations. This repository serves as a foundation for researchers and practitioners interested in exploring these intersections.
To get started with frp_rl, clone the repository and install the required dependencies. Follow these steps:
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Clone the repository:
git clone https://github.com/yourusername/frp_rl.git cd frp_rl -
Install the required packages:
pip install -r requirements.txt
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Ensure you have JAX installed. You can find the installation instructions here.
After installation, you can run the main script to see the free random projection in action. Here’s a simple example:
python main.py --config config.yamlYou can customize the configuration file to fit your needs. Check the config.yaml file for more options.
Free probability theory is a branch of mathematics that studies non-commutative random variables. It plays a crucial role in understanding the behavior of large random matrices and their applications in machine learning.
In-context reinforcement learning refers to the ability of agents to learn from context rather than explicit rewards. This approach can lead to more efficient learning processes, especially in complex environments.
JAX is a high-performance numerical computing library that enables automatic differentiation and GPU/TPU support. It is particularly useful for machine learning applications, allowing for faster experimentation and prototyping.
Random matrices are matrices whose entries are random variables. They are essential in understanding the properties of large datasets and have applications in various fields, including statistics and machine learning.
Spectral analysis involves studying the eigenvalues and eigenvectors of matrices. This technique is critical in understanding the behavior of random matrices and their impact on learning algorithms.
A state-space model represents a system using state variables. In reinforcement learning, these models help in formulating the environment and understanding the dynamics of the agent's actions.
Here’s a simple example of how to implement free random projection in a reinforcement learning setup:
from frp_rl import FreeRandomProjection
frp = FreeRandomProjection()
frp.fit(data)
projected_data = frp.transform(data)For more advanced usage, you can customize the parameters in the config.yaml file. Here’s an example configuration:
model:
type: "frp"
dimensions: 128
epochs: 100We welcome contributions to the frp_rl project. If you have ideas for improvements or new features, please follow these steps:
- Fork the repository.
- Create a new branch for your feature.
- Make your changes and commit them.
- Push your branch to your forked repository.
- Open a pull request to the main repository.
Please ensure that your code adheres to the existing style and includes tests where applicable.
This project is licensed under the MIT License. See the LICENSE file for details.
To download the latest release of frp_rl, visit the Releases section. Here, you can find the necessary files to get started.
For the most recent updates and changes, check the Releases page.
We would like to thank the contributors and the community for their support. Special thanks to the authors of the foundational papers in free probability and reinforcement learning, whose work inspired this project.
This README provides a comprehensive overview of the frp_rl project. For further details, feel free to explore the code and documentation. Your feedback and contributions are always welcome!