commonroad-rl 2022.2

Tools for applying reinforcement learning on commonroad scenarios.

CommonRoad-RL

This repository contains a software package to solve motion planning problems on CommonRoad using Reinforcement Learning algorithms. We currently use the implementation for the RL algorithms from OpenAI Stable Baselines, but the package can be run with any standard (OpenAI Gym compatible) RL implementations.

CommonRoad-RL in a nutshell

import gym
import commonroad_rl.gym_commonroad

# kwargs overwrites configs defined in commonroad_rl/gym_commonroad/configs.yaml
env = gym.make("commonroad-v1",
               action_configs={"action_type": "continuous"},
               goal_configs={"observe_distance_goal_long": True, "observe_distance_goal_lat": True},
               surrounding_configs={"observe_lane_circ_surrounding": True, "lane_circ_sensor_range_radius": 100.},
               reward_type="sparse_reward",
               reward_configs_sparse={"reward_goal_reached": 50., "reward_collision": -100})

observation = env.reset()
for _ in range(500):
    # env.render() # rendered images with be saved under ./img
    action = env.action_space.sample() # your agent here (this takes random actions)
    observation, reward, done, info = env.step(action)

    if done:
        observation = env.reset()
env.close()

Folder structure

commonroad-rl                                           
├─ commonroad_rl
│  ├─ doc                               # Folder for documentation         
│  ├─ gym_commonroad                    # Gym environment for CommonRoad scenarios
|     ├─ action                         # Action and Vehicle modules
|     ├─ observation                    # Observation modules
|     ├─ reward                         # Reward and Termination modules
|     ├─ utils                          # Utility functions for gym_commonroad
│     ├─ configs.yaml                   # Default config file for actions, observations, rewards, and termination conditions,
										  as well as for observation space optimization and reward coefficient optimization
│     ├─ commonroad_env.py              # CommonRoadEnv(gym.Env) class
│     └─ constants.py                   # Script to define path, vehicle, and draw parameters
│  ├─ hyperparams                       # Config files for default hyperparameters for various RL algorithms                                       
│  ├─ tests                             # Test system of commmonroad-rl.
│  ├─ tools                             # Tools to validate, visualize and analyze CommonRoad .xml files, as well as preprocess and convert to .pickle files.                                         
│  ├─ utils_run                         # Utility functions to run training, tuning and evaluating files                                      
│  ├─ README.md                                                      
│  ├─ evaluate_model.py                 # Script to evaluate a trained RL model on specific scenarios and visualize the scenario                
│  ├─ generate_solution.py              # Script to genearte CommonRoad solution files from trained RL models.
│  ├─ train_model.py                    # Script to train RL model or optimize hyperparameters or environment configurations           
│  ├─ sensitivity_analysis.py           # Script to run sensitivity analysis for a trained model
│  └─ plot_learning_curves.py           # Plot learning curves with provided training log files.                
├─ scripts                              # Bash scripts to install all dependencies, train and evaluate RL models, as well as generate CommonRoad solution files from trained RL models.
├─ README.md                                            
├─ commonroad_style_guide.rst           # Coding style guide for this project                
├─ environment.yml                                      
└─ setup.py                                      

Installation

Installation using Docker

Detailed instructions under ./commonroad_rl/install_docker/readme_docker.md

Installation via pip

1. download & install conda, and init anaconda to work from the terminal. tested on conda 4.5; 4.9, 4.10

~/anaconda3/bin/conda init
# for minconda
~/miniconda3/bin/conda init

2. setup a new conda env (or install packages to an existing conda env e.g. myenv conda env update --name myenv --file environment.yml)

conda create --name cr37 python==3.7
conda activate cr37
conda install mpi4py
pip install commonroad-rl

Usage

Tutorials

To get to know the package, please check ./commonroad_rl/tutorials for further details.

Python scripts

The commonroad_rl folder contains the source files. There are Python scripts for training, evaluating, and visualizing models. The most important scrips are explained in ./commonroad_rl/README.md and can be run with your Python executable. They are especially useful if you are developing a new feature or want to debug a specific part of the training.

Bash scripts

If you tested your codes already and everything runs smoothly on your computer and you now want to run the real experiments on larger dataset, the bash scripts help you with that. The are located in ./scripts. They can be used for training with PPO and TD3 and testing an agent. Always adapt the specific paths in the scripts to the corresponding paths on your machine and check the comments in the file to determine which arguments have to be provided.

Development

Please use development branch or open a new feature_xxx branch to make contribution.

References and Suggested Guides

1. OpenAI Stable Baselines: the implementation of RL algorithms used in our project.
2. OpenAI Spinning Up: we do not use their implementations in our project. But they provide quite nice explanations of RL concepts.
3. OpenAI Gym: general interface.
4. OpenAI Safety Gym: a special collection of Gyms for safe RL. Configurable as our project.

Publication

If you use CommonRoad-RL in your paper, please cite:

@inproceedings{Wang2021,
	author = {Xiao Wang and  Hanna Krasowski and  Matthias Althoff},
	title = {{CommonRoad-RL}: A Configurable Reinforcement Learning Environment for Motion Planning of Autonomous Vehicles},
	booktitle = {Proc. of the IEEE International Conference on Intelligent Transportation Systems (ITSC)},
	year = {2021},
}

Contact:

[e-mail]