A simple gym environment wrapping Carla, a simulator for autonomous driving research. The environment is designed for developing and comparing reinforcement learning algorithms. Trackable costs also enable the application of safe reinforcement learning algorithms.
Pixel: (3, 84, 84)
Index | Value |
---|---|
0 | r channel 84 x 84 |
1 | g channel 84 x 84 |
2 | b channel 84 x 84 |
State: (9, )
Index | Value |
---|---|
0 | x_pos |
1 | y_pos |
2 | z_pos |
3 | pitch |
4 | yaw |
5 | roll |
6 | acceleration |
7 | angular_velocity |
8 | velocity |
Action: (2, )
Index | Value | Min | Max |
---|---|---|---|
0 | throttle_brake | -1 | 1 |
1 | steer | -1 | 1 |
Add the following environment variables:
export PYTHONPATH=$PYTHONPATH:/opt/carla-simulator/PythonAPI
export PYTHONPATH=$PYTHONPATH:/opt/carla-simulator/PythonAPI/carla/
export PYTHONPATH=$PYTHONPATH:/opt/carla-simulator/PythonAPI/carla/dist/carla-0.9.9-py3.7-linux-x86_64.egg
Install the following extra libraries
pip install pygame
pip install networkx
pip install dotmap
pip install gym
Open a new terminal session, and run the CARLA simulator:
bash CarlaUE4.sh -fps 20
Open: carla_env/_init_.py
Insert a new environment configuration
register(
id='CarlaEnv-state-town01-v1',
entry_point='carla_env.carla_env:CarlaEnv',
max_episode_steps=500,
kwargs={
'render': True,
'carla_port': 2000,
'changing_weather_speed': 0.1,
'frame_skip': 1,
'observations_type': 'state',
'traffic': True,
'vehicle_name': 'tesla.cybertruck',
'map_name': 'Town01',
'autopilot': True
}
)
import gym
import carla_env
env = gym.make('CarlaEnv-pixel-v1')
env.reset()
done = False
while not done:
action = [1, 0]
next_obs, reward, done, info = env.step(action)
env.close()
Example script:
python carla_env_test.py
How to wrap the environment with the data collection wrapper:
env = gym.make('CarlaEnv-pixel-v1')
env = DataCollector(env, steps=200, save_dir='./output')
Load existing dataset:
env = DataCollector(env, steps=200, save_dir='./output', load_dir='./output/dataset_200.pkl')
Example script:
python collect_data_test.py
This repository is based on code from D4RL.