Learning to Play Games from Multiple Imperfect Teachers

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Examensarbete för masterexamen
Master Thesis
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Publicerad
2014
Författare
Karlsson, John
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Sammanfattning
This project evaluates the modularity of a recent Bayesian Inverse Reinforcement Learning approach [1] by inferring the sub-goals correlated with winning board games from observations of a set of agents. A feature based architecture is proposed together with a method for generating the reward function space, making inference tractable in large state spaces and allowing for the combination with models that approximate stateaction values. Further, a policy prior is suggested that allows for least squares policy evaluation using sample trajectories. The model is evaluated on randomly generated environments and on Tic-tac-toe, showing that a combination of the intentions inferred from all agents can generate strategies that outperform the corresponding strategies from each individual agent.
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Data- och informationsvetenskap, Computer and Information Science
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