janwithb / carla-gym-wrapper

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.

CARLA Gym Wrapper

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.

Features

• rendering
• weather
• different observation types (state, pixel)
• traffic
• autopilot
• vehicle and map selection
• configurable environments
• collect, save and load data
• costs (for safe RL)

Observation Space

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 Space

Action: (2, )

Index	Value	Min	Max
0	throttle_brake	-1	1
1	steer	-1	1

CARLA Setup

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

2. Install the following extra libraries

pip install pygame
pip install networkx
pip install dotmap
pip install gym

3. Open a new terminal session, and run the CARLA simulator:

bash CarlaUE4.sh -fps 20

Configure environments

1. Open: carla_env/_init_.py

2. 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
    }
)

Environment usage

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

Data collection

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

Credits

This repository is based on code from D4RL.