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An implementation of an Autonomous Vehicle Agent in CARLA simulator, using TF-Agents

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New version of Adaptive-Cruise Controller using DRL:

https://github.com/kochlisGit/Noise-Adaptive-Driving-Assistance-System

Self Driving Vehicles Using Deep Reinforcement Learning

** Description **

This is the project of my thesis. I've implemented an agent in CARLA Simulator, which is capable of navigating a vehicle safe & fast, using only 2 front cameras. More info about the simulator can be found here: https://carla.org/ . The agent has learnt to navigate in a lane using deep reinforcement learning algorithms. The development of the agent was made in Python.

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Demonstration

This directory contains 8 video files (mp4) that demonstrate how the vehicle is moving in the simulator https://github.com/kochlisGit/autonomous-vehicles-agent/tree/main/videos

The validation of the vehicle was done in a pre-defined route, that was new to the agent. The results are astonishing!

Python Libraries

  1. Carla API
  2. Numpy
  3. Matplotlib
  4. Tensorflow
  5. Keras
  6. TF-Agents
  7. Tensorflow-Addons

Sensors

  1. Collision Detector: https://carla.readthedocs.io/en/latest/ref_sensors/#collision-detector
  2. RGB Camera with Semantic Segmentation: https://carla.readthedocs.io/en/latest/ref_sensors/#semantic-segmentation-camera
  3. Depth Camera: https://carla.readthedocs.io/en/latest/ref_sensors/#depth-camera

Algorithms:

Execution

First, You have to download carla and all the libraries above. Then, download my "code" directory and paste it into "Carla/PythonAPI/". Run "agent/straight_lane_agent_c51_training.py" to start the training.

IMPORTANT The simulator window should be open, in order for the training to occur. Check https://carla.readthedocs.io/en/latest/start_introduction/ for more information of how to setup carla.

Libraries

  1. Python 3.7
  2. Carla <= 0.12
  3. Numpy >= 1.15
  4. Matplotlib
  5. Tensorflow >= 2.0
  6. TF-Agents >= 0.13
  7. Tensorflow-Addons >= 0.13 1 Opencv >= 4.0