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Playing the game of double pong combining learning and search

Background of thesis project
Suitable background
The thesis work will include various fields such as machine learning, simulation and optimization. Personal interest in programming is seen as benefit. The preferred language for coding is Java, but some other languages can be used if motivated.
Description of thesis work
To make a fleet of autonomous vehicles cost effective they need to act as a team. The reason is that decision of a single vehicle will more or less affect the operation of other vehicles.
This thesis focus on better understanding the machine learning technique Dyna2. Dyna2 combines learning and planning and has, for example, been succesfully been applied by Deep mind to the very challenging game board GO.
The double pong game is relevant to control of autonomous fleets because it is about coordinating objects. In the pong game, the controlled objects are two rackets. The game ends if the ball falls into the red area in the bottom of the screen. At all walls the ball will bounce. The objective is to move the padels so the game continous as for long time as possible.
By search it should be possible to identify the adequate short term actions of the objects. In the figure above: move the left padel to a more right position. The adequate horizon of the search is an open question.
Learning is about remembering good and bad states of the objets. In the game of double pong it is probably a bad state to let both rackets be at the same positions.
This master thesis handles the following questions:
  1. What are the adequate input signals to the artifical double pong player? An example signal is the ball x-position..
  2. How shall double pong physics be modelled?
  3. How shall bad states and actions be differentiated?
  4. How could the search per performed?
  5. How can the learning be performed? One can for example thing about training a feed forward neural network.
  6. How can differente problem approaches be compared? Compuational burden might be one aspect. A search horizon of 10 is more demanding compared to a horizon of 1.

The thesis work will include various fields such as machine learning, simulation and optimization. Personal interest in programming is seen as benefit. The prefered language for coding is Java, but some other languages can be used if motivated.
Thesis Level: Master and/or Bachelor
Language: English
Starting date: Nov 20201 - Jan 2022
Number of students: 1-2
Tutor: Jonas, Dr., 0739-024761

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