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Erschienen in:
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2016 | OriginalPaper | Buchkapitel

Combining Case-Based Reasoning and Reinforcement Learning for Tactical Unit Selection in Real-Time Strategy Game AI

verfasst von : Stefan Wender, Ian Watson

Erschienen in: Case-Based Reasoning Research and Development

Verlag: Springer International Publishing

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Abstract

This paper presents a hierarchical approach to the problems inherent in parts of real-time strategy games. The overall game is decomposed into a hierarchy of sub-problems and an architecture is created that addresses a significant number of these through interconnected machine-learning (ML) techniques. Specifically, individual modules that use a combination of case-based reasoning (CBR) and reinforcement learning (RL) are organised into three distinct yet interconnected layers of reasoning. An agent is created for the RTS game StarCraft and individual modules are devised for the separate tasks that are described by the architecture. The modules are individually trained and subsequently integrated in a micromanagement agent that is evaluated in a range of test scenarios. The experimental evaluation shows that the agent is able to learn how to manage groups of units to successfully solve a number of different micromanagement scenarios.

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Vorheriges Kapitel Relevance Matrix Generation Using Sensitivity Analysis in a Case-Based Reasoning Environment
Nächstes Kapitel Defining the Initial Case-Base for a CBR Operator Support System in Digital Finishing
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Metadaten
Titel
Combining Case-Based Reasoning and Reinforcement Learning for Tactical Unit Selection in Real-Time Strategy Game AI
verfasst von
Stefan Wender
Ian Watson
Copyright-Jahr
2016
Buch
Case-Based Reasoning Research and Development

Print ISBN: 978-3-319-47095-5

Electronic ISBN: 978-3-319-47096-2

Copyright-Jahr: 2016

DOI
https://doi.org/10.1007/978-3-319-47096-2_28