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REINFORCEMENT LEARNING OF STRATEGIES FOR SETTLERS OF CATAN

REINFORCEMENT LEARNING OF STRATEGIES FOR SETTLERS OF CATAN,Michael Pfeiffer

REINFORCEMENT LEARNING OF STRATEGIES FOR SETTLERS OF CATAN   (Citations: 8)
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In this paper we study the application of machine learning methods in complex computer games. A combination of hierarchical reinforcement learning and simple heuristics is used to learn strategies for the game Settlers of Catan (© 1995 by Kosmos Verlag, Stuttgart) via self-play. Since existing algorithms for function approximation are not well-suited for problems of this size and complexity, we present a novel use of model trees for state-action value prediction in a sophisticated computer game. Furthermore we demonstrate how a- priori knowledge about the game can reduce the learning time and improve the performance of learning virtual agents. We compare several different learning approaches, and it turns out that, despite the simplicity of the architecture, a combination of learning and built- in knowledge yields strategies that are able to challenge and even beat human players in a complex game like this.
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    • ...Few research papers are available on autonomous learning in Settlers of Catan [12], and according to the results reported therein, they are far from reaching human-level play yet...
    • ...Pfeiffer [12] also used the JSettlers environment to implement a learning agent...

    István Szitaet al. Monte-Carlo Tree Search in Settlers of Catan

    • ...There have been attempts to apply self-play procedures to computer games where the search space is impossibly large [5], and the limited objective of discovering playing strategies through the use of self-playing agents is thought to be achievable...
    • ...Some form of intelligence is required in the agents used to test the game and this use of AI may be counterintuitive for many games developers who consider the introduction of artificial players or agents solely to impart interest (although Pfeiffer, for example used this principle of self-play with learning agents to learn strategies for complex computer games ‐ the particular example cited is ‘Settlers of Catan’ [5])...

    Alasdair Macleod. Game design through self-play experiments

    • ...cess. In [Pfeiffer, 2004] a hierarchical reinforcement learning approach was used to create a program which plays a board game called The Settlers of Catan, which is a popular modern board...

    Stephen Robertson. Using Hierarchical Reinforcement Learning to Balance Conflicting Sub-P...

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