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Learning a Transferable World Model by Reinforcement Agent in Deterministic Observable Grid-World Environments
2012
Information Technology and Control
Reinforcement-based agents have difficulties in transferring their acquired knowledge into new different environments due to the common identities-based percept representation and the lack of appropriate generalization capabilities. In this paper, the problem of knowledge transferability is addressed by proposing an agent dotted with decision tree induction and constructive induction capabilities and relying on decomposable properties-based percept representation. The agent starts without any
doi:10.5755/j01.itc.41.4.915
fatcat:5pgeedmyjbhdzgmidzvkyneh4i