XRouting: An explainable vehicle rerouting system based on reinforcement learning with Transformer structure
If you use our code, please consider cite the following: Z. Wang and S. Wang, "XRouting: Explainable Vehicle Rerouting for Urban Road Congestion Avoidance using Deep Reinforcement Learning," 2022 IEEE International Smart Cities Conference (ISC2), Pafos, Cyprus, 2022, pp. 1-7, doi: 10.1109/ISC255366.2022.9922404.
@INPROCEEDINGS{9922404,
author={Wang, Zheng and Wang, Shen},
booktitle={2022 IEEE International Smart Cities Conference (ISC2)},
title={XRouting: Explainable Vehicle Rerouting for Urban Road Congestion Avoidance using Deep Reinforcement Learning},
year={2022},
volume={},
number={},
pages={1-7},
doi={10.1109/ISC255366.2022.9922404}}
Installation instructions are provided for MacOS Monterey 12.4. In order to reproduce the results, the traffic scenario simulator SUMO with version 1.13.0 and the reinforcement learning training tool RLlib with version 1.12.0 should be installed. Besides, the version of python is highly recommended to be 3.8. The installation steps are elaborated as follows.
- Users can install SUMO by following the
macOS sectionin https://sumo.dlr.de/docs/Installing/index.html#macos . Note that users are strongly recommended to setSUMO_HOME directorycarefully. - Users can install Ray/RLlib by following https://docs.ray.io/en/latest/ray-overview/installation.html . Note that
Installing Ray with Anacondais highly suggested. - It is equally important that the version of tensorflow should be
2.7+. - The versions of xlrd and xlwt should be
1.2.0for the sake of sucessful running. - Note that after installing SUMO, it is prerequisite for users to modify
line 18in/rl/env/multi_agent_rerouting_env.pyandline 21in/rl/env/dynamic_rerouting_env.pyfor the sake of importing SUMO packages for SUMO and python connection. - Note: All the training and evaluation processes should be conducted under the downloaded repository directory.
The training scenario is defined in the directory \sumo_scenario\, including the configuration files scenario.sumocfg.xml for training stage and scenario_rl.sumocfg.xml for evaluation stage, scenario.net.xml for traffic netwrok construction, exercise.add.xml for necessary components and scenario.trips.xml for determining traffic trips. Note that edge_coordinates.xlsx is stored in this directory as well, which is used to store the coordinates information of all edges. The training scenario is illstrated as:
In order to train the models including the proposed XRouting and the other two comparisms (normal PPO and DQN), users are highly recommended to run the train.py file in virtue of terminal by following the command:
python train.py --run=XRouting
Note that input argument --run is used to indicate the model to be trained. If users desire to train normal PPO and DQN, the value of --run should be set as PPO and DQN respectively. The default value is XRouting. Moreover, there are other argements that could be claimed by users, whose names and meanings can be found by following the command:
python train.py -h
Note that users can visualize training performance by running tensorboard --logdir [directory] in a seperate terminal, where [directory] is defaulted to \training_result\XRouting\PPO for XRouting model, \training_result\PPO\PPO for PPO model and \training_result\DQN\DQN for DQN model.
Another important training results are information of trips. More specifically, the basic trip information for each vehicle, including travelling time and travelling length, of each episode during training process is stored in the directory \training_tripinfo\XRouting_training for XRouting model, \training_tripinfo\PPO_training for PPO model and \training_tripinfo\DQN_training for DQN model.
Though XRouting model for the aforementioned traffic scenario has been well tuned, users are still able to tune the hyperparameters used for training. More specifically, all the three models hyperparameters tuning can be implemented in the directory \rl\model_config.py. For example, the hyperparameters setting for XRouting is illustrated as:
def XRouting_config(self, env_name):
"""
Configuration of XRouting actor
"""
ModelCatalog.register_custom_model("XRouting_model", XRoutingModel)
return {
"env": env_name,
"num_gpus": self.num_gpus,
"num_envs_per_worker": 1,
"gamma": 0.99,
"train_batch_size": 4096,
"sgd_minibatch_size": 256,
"lr": 4e-4,
"vf_loss_coeff": 1e-5,
"model": {
"custom_model": "XRouting_model",
"custom_model_config": {
"attention_dim": 64,
"num_heads": 4,
"head_dim": 32,
"mlp_dim": 100,
"observation_dim": (38, 6),
"pos_encoding_dim": (38, 1),
}
},
"entropy_coeff": 0.01,
"num_sgd_iter": 4,
"framework": "tf",
"num_cpus_per_worker": self.num_cpus_per_worker,
}For the meaning and function of each hyperparameter, it is strongly recommended that users can search them by following the url: https://docs.ray.io/en/latest/rllib/rllib-algorithms.html .
After obtaining the trained models, users can evaluate the models in multi-agent scenario by using the following command
python evaluation_multi_agent.py --run=XRouting
In the evaluation stage, both scenario_rl.sumocfg.xml and scenario_rl.trips.xml are used instead, and scenario_rl.trips.xml defines 50% penetration, which is generated randomly. Furthermore, SUMO_GUI is used for visulization because of just one episode.
Note that the four well trained models have already been stored in the directory /trained_models/XRouting/, and the default model used is /trained_models/XRouting/checkpoint_1/checkpoint-50. Userd can also utilize the specific trained model in virtue of the argument --checkpoint-dir.
All the evaluation results, that is, the excel files which contains the input observations and the corresponding actions of all the steps that rl vehicles take, are stored in the directory /evaluation_results/.