Reasoning about Unforeseen Possibilities During Policy Learning

10 Jan 2018Craig InnesAlex LascaridesStefano V AlbrechtSubramanian RamamoorthyBenjamin Rosman

Methods for learning optimal policies in autonomous agents often assume that the way the domain is conceptualised---its possible states and actions and their causal structure---is known in advance and does not change during learning. This is an unrealistic assumption in many scenarios, because new evidence can reveal important information about what is possible, possibilities that the agent was not aware existed prior to learning... (read more)

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