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Learning a Super Mario controller from examples of human play | IEEE Conference Publication | IEEE Xplore

Learning a Super Mario controller from examples of human play


Abstract:

Imitating human-like behaviour in action games is a challenging but intriguing task in Artificial Intelligence research, with various strategies being employed to solve t...Show More

Abstract:

Imitating human-like behaviour in action games is a challenging but intriguing task in Artificial Intelligence research, with various strategies being employed to solve the human-like imitation problem. In this research we consider learning human-like behaviour via Markov decision processes without being explicitly given a reward function, and learning to perform the task by observing expert's demonstration. Individual players often have characteristic styles when playing the game, and this method attempts to find the behaviours which make them unique. During play sessions of Super Mario we calculate player's behaviour policies and reward functions by applying inverse reinforcement learning to the player's actions in game. We conduct an online questionnaire which displays two video clips, where one is played by a human expert and the other is played by the designed controller based on the player's policy. We demonstrate that by using apprenticeship learning via Inverse Reinforcement Learning, we are able to get an optimal policy which yields performance close to that of an human expert playing the game, at least under specific conditions.
Date of Conference: 06-11 July 2014
Date Added to IEEE Xplore: 22 September 2014
ISBN Information:

ISSN Information:

Conference Location: Beijing, China

I. Introduction

The game industry has been rapidly expanding for the past few decades and it is the fastest-growing component of the international media sector. It has been devoting considerable resources to design highly sophisticated graphical content and challenging and believable Artificial Intelligence (AI). Various artificial intelligence methods have been employed in modern video games to engage players longer, game agents built with human-like behaviour and cooperation, which raise the players' emotional involvement and increase immersion in the game simulation.

References

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