From fcee18fa0a55acec04994b7fcf053c54fa124701 Mon Sep 17 00:00:00 2001 From: "Egli Adrian (IT-SCI-API-PFI)" <adrian.egli@sbb.ch> Date: Wed, 22 May 2019 09:58:24 +0200 Subject: [PATCH] text enhancement --- README.rst | 15 +++++++++------ 1 file changed, 9 insertions(+), 6 deletions(-) diff --git a/README.rst b/README.rst index 07ed6a56..915d3c83 100644 --- a/README.rst +++ b/README.rst @@ -25,12 +25,15 @@ It can be used for many learning task where a two-dimensional grid could be the Flatland delivers a python implementation which can be easily extended. And it provides different baselines for different environments. Each environment enables an interesting task to solve. For example, the mutli-agent navigation task for railway train dispatching is a very exciting topic. It can be easily extended or adapted to the airplane landing problem. This can further be the basic implementation for many other task in transporation and logcistics. -The railway environment has a very restricted transition behaviour. Trains can normally not run backwards and the have to follow rails. -The can only switch cells along rails or the pass a switch in right direction. Thus the navigation behaviour of a train is very restricted. -The planning problem where many agents share same infrastructure becomes mostly to an ordering problem. -Trains have a departing location and a destination where they have to travel to. The agents have to learn to avoid each others or to learn to pass. -Otherwise the can never successfully reach the destinations. In complex situation they have to learn to cooperate otherwise they get stocked in dead-lock situation. -This make the railway planning problem a very complex mulit-agent reinforcement task. + +Mapping a railway infrastructure into a grid world is an excellent example showing how the movement can of an agent can be easily restricted with the help of the cell's transition maps. +As trains can normally not run backwards and they have to follow rails the transition for one cell ot the other depends also the train's orientation. +Trains can only change the traveling path at switches. There a two variants of switches. The first kind of switch is the splitting "switch", where trains can change rails and in consequence the traveling path. +The second kind of swtich is the fusion switch, where train can change order. That means two rails come together. Thus the navigation behaviour of a train is very restricted. +The railway planning problem where many agents share same infrastructure is a very complex problem. If trains can not change traveling path, the underlaying problem will be an ordering problem. Even the ordering +problem is very hard to solve. +Furthermore trains have a departing location where they can not depart earlier than a committed time. Then the have to arrive at destination not later than the second committed time. This makes the whole planning problem +still more complicated. In such a complex environment cooperation is essential. Thus agents have to learn to cooperate in a way that all trains (agents) arrive on time. Getting Started -- GitLab