/SUMO-changing-lane-agent

Implementation of a reinforcement learning agent able to do autonomous changing lane using Sumo

Primary LanguagePython

Training RL Agent with SUMO simulator

Objective

This Project is inspired from this project (https://github.com/federicovergallo/SUMO-changing-lane-agent)

The project aims at developing a reinforcement learning application to make an agent drive safely in condition of differnet densitiy of traffic. For doing so, SUMO was used to simulate the behaviour of the ego vehicletogether with a fleet of autonomous vehicles and to train model of RL algorithms.The development flow was the following: creating the highway with NetEdit, get the parameters of thesimulation, create a custom Gym environment and train the networks

Main contributions of this environment:

  1. It is easy to configurate. Compared with FLOW, it is easy to directly control SUMO vehicles

  2. It is easy to extend to different real road networks, we could use open street map to download the network in net.xml format and add the rou.xml file to generate the traffic flow.

  3. The Autonomous vehicle can be controlled by a. rule-based controller; b. RL controller; c. user's keyboard.

  4. It is possible to extend to control by large language model

Single agent Example scenario

  1. Loop network: this network can support long running steps to gather enough experience for agent to learn. We can also support changing differnet traffic density to evaluate the driving efficiency and safety. In this network, the curvature's effect on speed is disabled.

1.1 Normal Loop network:

Alt text

1.2 Loop network with stop and go:

Alt text

  1. Winston Churchill eastbound on-ramp QEW road geometry: To further evaluate the effectiveness of the proposed method, we create another network based on real road geometry, i.e., Queen Elizabeth Way (QEW) Winston Churchill eastbound on-16 ramp near Toronto, Canada. This network can apply emergent brake, stay in main lane and choose to exit.

2.1 stay in main lane in qew Alt text

2.2 choose to exit in qew Alt text

2.3 emergent brake in qew Alt text

2.4 merge in qew Alt text

2.5 intersection in qew Alt text

Multi agent Example scenario

3.1 Loop network with multi-agent:

Alt text

3.2 Merge network with multi-agent:

Alt text

Installation

  1. First install SUMO, please follow this https://sumo.dlr.de/docs/Installing/Linux_Build.html

  2. Check sumo version: (you could also use other version)

Eclipse SUMO sumo Version v1_16_0+1958-0ab20a374a1 Build features: Linux-5.19.0-35-generic x86_64 GNU 11.3.0 Release FMI Proj GUI Intl SWIG GDAL GL2PS Eigen Copyright (C) 2001-2023 German Aerospace Center (DLR) and others; https://sumo.dlr.de License EPL-2.0: Eclipse Public License Version 2 https://eclipse.org/legal/epl-v20.html Use --help to get the list of options.

  1. Remember to change sumo path to your own in the gym_sumo/gym_sumo/envs/

  2. And then the required packages from environment.yml, i.e., conda env create -f environment.yml

  3. Remember to install gym-sumo, go to gym_sumo and run pip install -e .

others

main.py to train model

test.py to test model

main_step.py to check with environment setup and visualization, which include different lane change controller or car following controller

agents/ it integrate differnet possible control algorithm, such as RL models or formula-based driving models

RL algorithms modified based on this repo: https://github.com/seolhokim/Mujoco-Pytorch

To Do:

Need to debug the action space, decide on and implement the obs space, and implement the reward

Multi-agent visualization (on going)

Add LLM control