Reinforcement Learning for Railway Scheduling · Reinforcement Learning for Railway Scheduling...

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Reinforcement Learning

for Railway Scheduling

Overcoming Data Sparseness through Simulations

Dr. Erik Nygren Research and Innovation Lab Swiss Federal Railway

© SBB • Solution Center Infrastructure • Research & Innovation • October 2017 2

Swiss Railway Network. A Complex Dynamical System.

Influencing Factors Facts

1

10,000

1,210,000

Weather People

Infrastructure Events

12,997

Most dense network 33,000

210,000 t

1t

31,266

3,230 km

KM

Energy

© SBB • Solution Center Infrastructure • Research & Innovation • October 2017 3

Train Dispatching and Scheduling. Challenges in the Worlds Densest Train Network.

RCS

Train runs

Production

Timetable

Evolution of Dispatching. Towards Full Automation.

Today

Future

Past

© SBB • Solution Center Infrastructure • Research & Innovation • October 2017 4

© SBB • Solution Center Infrastructure • Research & Innovation • October 2017 5

Automated Train Dispatching. Current Challenges.

Big Data

Big Data: Not enough relevant information

Automated

Dispatching

Learning

Measure-

ments Action

© SBB • Solution Center Infrastructure • Research & Innovation • October 2017 6

Reinforcement Learning for Railway Dispatching. Overcoming Data Sparseness through Simulations.

WIP

Measure- ments Action

Validation

Data generation

Learning

Learning

Action

Artificial Data

Big Data

High Performance Simulation

© SBB • Solution Center Infrastructure • Research & Innovation • October 2017 7

High Performance Simulations. Unleashing the Power of Parallel Computing.

DGX-1 High Performance Simulations

Time speedup Scenario variations Influencing factor analysis

© SBB • Solution Center Infrastructure • Research & Innovation • October 2017 8

Preliminary Results. Visualization of Simulation Results.

2h realtime

500x

5000x Simulation speed

Visualization speed

© SBB • Solution Center Infrastructure • Research & Innovation • October 2017 9

Reinforcement Learning. Playing the Dispatcher Game.

Action

Reward

DGX-1 High Performance Simulations

Artificial Data

DGX-1 Automated Dispatcher

© SBB • Solution Center Infrastructure • Research & Innovation • October 2017 10

Machine Learning on Artificial Data. Generating, Evaluating and Optimizing Train Dispatching.

Automated Dispatcher

Reinforcement Learning

Tree Search

Evolutionary Strategies

Building Blocks Variable Topologies

1

2

3

Mixed Integer Linear Programming

Genetic Algorithm

© SBB • Solution Center Infrastructure • Research & Innovation • October 2017 11

Current State... And Future Expected Reward.

DGX-1 High Performance Simulations

DGX-1 Automated Dispatcher

Fully Automated Process

Train runs

Production

Timetable

Take Home.

Big Data Big Information

© SBB • Solution Center Infrastructure • Research & Innovation • October 2017 13

Take Home.

AI

Model

Big Data Big Information

Dr. Erik Nygren

erik.nygren@sbb.ch

AI Researcher

Research Team

© SBB • Solution Center Infrastructure • Research & Innovation • October 2017 14

Reward Function. How to Reward an Artificial Dispatcher.

Reward

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