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A Deep Reinforcement Learning Approach to Behavioral Planning for Autonomous Vehicles

Behavioral planning for autonomous vehicles in scenarios such as intersections and lane-changing/merging is a challenging task. The code in this repository is for a couple of the experiments performed for this paper.

Installation

The code of this repo has been tested on Ubuntu 14.04 and with Python 2.7

  1. Install SUMO 0.30

    Execute the following:

    sudo apt-get install sumo sumo-tools sumo-doc

    sudo add-apt-repository ppa:sumo/stable

    sudo apt-get update

    sudo apt-get install sumo sumo-tools sumo-doc

    Please make sure these instructions are followed exactly.

  2. Additional Python Packages install via pip:

    pip install moviepy imageio tqdm tensorflow==1.4 requests scipy matplotlib ipython

  3. Include export SUMO_HOME=/usr/share/sumo in your ~/.bashrc and source ~/.bashrc

Training

To train the DRQN: Add a folder called Center in the gym-traffic folder. In the Center folder, create a subdirectory called frames. This is where the learning result frames are stored. Create a folder in the examples folder called drqn. This is where the weights checkpoint files are stored. The checkpoint files are created every 100 episodes, so you must train at least 100 in order to run the DRQN.

cd [repo_root]/gym-traffic/examples

python example_gym_traffic_drqn.py train

Experiments

For each of these experiments, you will probably want to set the delay to 0ms once the GUI opens. This will decrease the amount of time you spend waiting for the ego-vehicle to reach the intersection. The simulation should slow down when the ego-vehicle arrives, but you may still need to manage the speed manually via the delay setting.

To test a random routine:

cd [repo_root]/gym-traffic/examples

python example_gym_traffic_random.py

To test a TTC (Time to Collision) rule based approach:

cd [repo_root]/gym-traffic/examples

python example_gym_traffic_ttc.py

To test the DRQN (Deep Recurrent Q-Network) based approach (you must have trained at least 100 episodes before running this):

cd [repo_root]/gym-traffic/examples

python example_gym_traffic_drqn.py test

To run with the GUI enabled:

python example_gym_traffic_drqn.py test --gui