Learning to Play Games from Multiple Imperfect Teachers
Ladda ner
Typ
Examensarbete för masterexamen
Master Thesis
Master Thesis
Program
Complex adaptive systems (MPCAS), MSc
Publicerad
2014
Författare
Karlsson, John
Modellbyggare
Tidskriftstitel
ISSN
Volymtitel
Utgivare
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.
Beskrivning
Ämne/nyckelord
Data- och informationsvetenskap , Computer and Information Science