Safety-autonomous-driving-Dense-Deep-reinforcement-learning-Highway-env [Big data & HPC]

University Of Liverpool - [Naga sri ram Kochetti] [201664307]

This repository is dedicated to the advancement of autonomous vehicle safety through the application of dense reinforcement learning. Leveraging the power of the highway-env environment, our goal is to create a robust validation framework that ensures the safety of autonomous vehicles under a wide range of driving scenarios.

Safety in Autonomous Driving using Dense Deep Reinforcement Learning with Highway-env

Description

This repository focuses on improving the safety of autonomous vehicles through the application of dense deep reinforcement learning. By harnessing the capabilities of the highway-env environment, our project aims to establish a robust safety validation framework to assess the performance of autonomous vehicles in diverse driving scenarios.

Table of Contents

• Installation
• Usage
• Features
• Contribution Guidelines
• Acknowledgements

Installation

1. Clone the repository:
   git clone https://github.com/nagasriramnani/Safety-autonomous-driving-Dense-Deep-reinforcement-learning-Highway-env.git

• 2.Install dependencies: -conda create --name highwayenv python==3.9
• pip install tensorflow
• pip install gymnasium
• pip install gym
• pip install pygame
• pip install pytorch
• pip install stable-baselines3[extra]
• pip install stable-baselines3

Usage

3. activate conda environmet using conda activate your-env-name 4.To check the environment how it works .py files are given in project directory
4. After completing the basic setup to test the files, which are A2c_test.ipynb ,epslion_greeady.ipynb , PPo_test.ipynb.

Features

• Dense Reinforcement Learning Integration: Implement state-of-the-art reinforcement learning algorithms tailored for autonomous vehicle safety validation.
• Highway-env Integration: Use the highway-env environment for simulating a plethora of traffic situations.
• Visualization Tools: Comprehensive tools to visualize and understand the agent's decision-making processes.
• Evaluation Metrics: Metrics to assess safety, efficiency, and overall performance of the autonomous agents.

Contribution Guidelines

We welcome contributions from the community! If you'd like to contribute, please follow these steps:

1. Fork the repository.
2. Make your changes.
3. Submit a pull request with a detailed description of your changes.

Acknowledgements

Special thanks to Leurent, Edouard for creating the highway-env environment which serves as the backbone for our simulations.

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