2d-walking

2d-walking with reinforcement learning and development

Description

This project aims to create a 2D walking model for robotics using reinforcement learning. The goal is to explore different types of walking and integrate them into this model. The project includes the implementation of a Soft Actor-Critic (SAC) agent to achieve digital walking.

Installation

1. Clone the repository:

   git clone https://github.com/VishwamAirobotics/2d-robots.git
   cd 2d-robots

2. Create a virtual environment:

   python3 -m venv venv
   source venv/bin/activate

3. Install the required packages:

   pip install -r requirements.txt

Usage

1. Train the agent:

   python agents/train_agent.py

2. Evaluate the agent:

   python agents/evaluate_agent.py

Running Tests

To run the tests locally, use the following command:

pytest

Continuous Integration

This project uses GitHub Actions for Continuous Integration (CI). The CI workflow is defined in the .github/workflows/ci.yml file. It includes the following steps:

• Set up Python environment
• Install dependencies
• Lint the code with flake8
• Run tests with pytest

Contributing

Contributions are welcome! Please fork the repository and create a pull request with your changes.

License

This project is licensed under the MIT License.