THIS REPOSITORY HAS BEEN MOVED TO https://github.com/AlgTUDelft/cTORS

Treinonderhoud- en -rangeersimulator (TORS)

This implementation of TORS consists of a backend written in C++ (cTORS), and a front-end written in python (TORS).

Project setup

The basic project setup uses the structure provided by cmake. The subfolders are subprojects:

• cTORS: The c++ implementation of TORS
• cTORSTest: The tests for cTORS
• pyTORS: The python interface for cTORS
• TORS: The challenge environment, in python

Installation

The following section explains how to compile this source code. Alternatively one can run the code in a docker container. The Dockerfile also shows how this project can be compiled and run step-by-step.

Install Cmake and Python development libraries

To compile cTORS, cmake 3.11 (or higher) is required and the python development libraries:

apt-get install cmake
apt-get install python3-dev

For windows:

• download and install cmake: https://cmake.org/download/
• python header files already installed with python, see the include folder in your python folder.

Install Google Protocol Buffers

This project uses Google Protocol Buffers to read the data files. Installation is required to compile the C++ source:

• Download the source from the github page (version 3.15.6 is used)
• Follow the instructions to install

Build with setuptools

You can build cTORS and the pyTORS library with the following command.

mkdir build
python setup.py build
python setup.py install

Compile cTORS from C++ source

In the source directory execute the following commands:

mkdir build
cd build
cmake ..
cmake --build .

This has been tested with gcc 9.3. Older versions may not support the c++17 standard.

Alternative: build with docker

You can also run TORS in a docker container. To build and run the container, run:

docker build -t tors-base .
docker run --network="host" --rm -it tors-base /bin/bash

Basic usage

Run the challenge environment

To run challenge environment, run the following code

cd TORS
python run.py

Optionally you can change the episode or agent data by changing the parameters

python run.py --agent agent.json --episode episode.json

The --agent option sets the file that configures the agent. The --episode option sets the file that configures the episode.

You can also run the file with the --train flag to train the agent instead of eveluating its performance.

Usage in Python

To use cTORS in python, you need to import they pyTORS library. E.g.

from pyTORS import Engine
 
engine = Engine("data/Demo")
scenario = engine.get_scenario("data/Demo/scenario.json")
state = engine.start_session(scenario)
 
actions = engine.step(state)
engine.apply_action(actions[0])
 
engine.end_session(state)

Running the visualizer

The visualizer runs as a flask server. Install the dependencies in TORS/requirements-visualizer first.

pip install -r TORS/requirements-visualizer

Now flask can be run by running the commands:

cd TORS/visualizer
export FLASK_APP=main.py
export FLASK_ENV=development
export FLASK_RUN_PORT=5000
python -m flask run

Running the example RL-agent with gym

The repository also includes example code that wraps cTORS in a gym-environment and uses an RL implementation from stable-baselines3 to learn a policy. To run this example, first install the requirements:

pip install -r TORS/requirements-gym

Then run:

cd TORS
python run_gym.py

You can check the learning progress using tensorboard:

tensorboard --logdir ./log_tensorboard/

Configuration

TORS can be configured through configuration files. Seperate configuration exists for

1. The location
2. The scenario
3. The simulator

1. The episode
2. The agent

Configuring the location

A location is described by the location.json file in the data folder. It describes the shunting yard: how all tracks are connected, what kind of tracks they are, and distances among tracks.

In order to use the visualizer for that location, you need to provided another file vis_config.json. See the folder data/Demo and data/KleineBinckhorstVisualizer for examples.

Configuring the scenario

A scenario is described by the scenario.json file in the data folder. It describes the scenario: which employees are available, shunting units' arrivals and departures, and possible disturbances.

Configuring the simulator

The simulator can be configured by the config.json file in the data folder. It describes which business rules need to be checked and the parameters for the actions

Configuring the episode

You can provide an episode configuration and pass it to TORS/run.py with the --episode parameter. This file describes the evaluation/training episode. It contains the path to the data folder, the number of runs, RL parameters and parameters for scenario generation.

Configuring the agent

You can provide an agent configuration and pass it to TORS/run.py with the --agent parameter. This file prescribes which agent to use, and passes parameters to the agent.

Tests

Run the cTORS tests

To run the cTORS tests, execute the commands

cd build
ctest

Contributors

• Mathijs M. de Weerdt: Conceptualization, Supervision, Project administration, Funding acquisition, Writing - review & editing
• Bob Huisman: Conceptualization
• Koos van der Linden: Software, Writing - Original draft
• Jesse Mulderij: Writing - Original draft
• Marjan van den Akker: Supervision of the bachelor team
• Han Hoogeveen: Supervision of the bachelor team
• Joris den Ouden: Conceptualization, Supervision of the bachelor team
• Demian de Ruijter: Conceptualization, Supervision of the bachelor team
• Bachelor-team, consisting of Dennis Arets, Sjoerd Crooijmans, Richard Dirven, Luuk Glorie, Jonathan den Herder, Jens Heuseveldt, Thijs van der Horst, Hanno Ottens, Loriana Pascual, Marco van de Weerthof, Kasper Zwijsen: Software, Visualization