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driving_in_traffic

README.md

Driving in Traffic

This example illustrates the training of an ego agent to drive, as fast and as far as possible, in traffic using the DreamerV2 reinforcement-learning algorithm.

The ego agent earns reward for the distance travelled per-step and is penalised for colliding with other vehicles and for going off-road.

Trained agent driving in traffic

Observation space

  • Topdown RGB image
    • size (width x height): 64 pixels x 64 pixels
    • resolution: 1 meter/pixel
observation_space = gym.spaces.Dict({
    gym.spaces.Box(low=0, high=255, shape=(64,64,3), dtype=np.uint8),
})

Action space

  • Throttle: [0,1]
  • Brake: [0,1]
  • Steering: [-1,1]
action_space = gym.spaces.Box(low=-1.0, high=1.0, shape=(3,), dtype=np.float32)

Setup

$ git clone https://github.com/huawei-noah/SMARTS.git
$ cd <path>/SMARTS/examples/driving_in_traffic
$ python3.7 -m venv ./.venv
$ source ./.venv/bin/activate
$ pip install --upgrade pip
$ pip install -e .

Train

  1. Train 
    $ cd <path>/SMARTS/examples/driving_in_traffic
$ python3.7 run.py
  2. The trained model is saved into <path>/SMARTS/examples/driving_in_traffic/logs/<folder_name> folder.

Evaluate

  1. Evaluate 
    $ cd <path>/SMARTS/examples/driving_in_traffic
$ scl envision start -s ./scenarios &
$ python3.7 run.py --mode=evaluate --logdir="<path>/SMARTS/examples/driving_in_traffic/logs/<folder_name>" --head
  2. Go to localhost:8081 to view the simulation in Envision.

Docker

  1. Train a model inside docker 
    $ cd <path>/SMARTS
$ docker build --file=<path>/SMARTS/examples/driving_in_traffic/Dockerfile --network=host --tag=driving_in_traffic <path>/SMARTS
$ docker run --rm -it --network=host --gpus=all driving_in_traffic
(container) $ cd /src/examples/driving_in_traffic
(container) $ python3.7 run.py