pgkang / hybrid-deeprl-automated-driving

Integrating Deep Reinforcement Learning with Path planning for Automated Driving

A Hybrid Deep Reinforcement Learning Based Automated Driving Agent for CARLA

Codebase for our Hybrid Deep Reinforcement Learning (H-DRL) based automated driving project. The related paper can be accessed with this link.

Work in progress. Stay tuned for the full release.

Features

Overview

An overview of our framework. The proposed system is a hybrid of a model-based planner and a model-free DRL agent. *Other sensor inputs can be anything the conventional pipe needs. ** We integrate model-based planners into the DRL agent by adding "distance to the closest waypoint" to our state-space, where the path planner gives the closest waypoint. Furthermore, the reward function is modified accordingly: the agent is penalized for straying away from the model-based planners' waypoints and also making a collision. Any kind of path planner can be integrated into the DRL agent with the proposed method.

Installation

Credits

This project was forked from a conventional DRL implementation for CARLA by Sentdex. https://github.com/Sentdex/Carla-RL