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Curiosity for machine agents has been a focus of intense research. The studyof human and animal curiosity, particularly specific curiosity, has unearthedseveral properties that would offer important benefits for machine learners,but that have not yet been well-explored in machine intelligence. In this work,we introduce three of the most immediate of these properties -- directedness,cessation when satisfied, and voluntary exposure -- and show how they may beimplemented together in a proof-of-concept reinforcement learning agent;further, we demonstrate how the properties manifest in the behaviour of thisagent in a simple non-episodic grid-world environment that includescuriosity-inducing locations and induced targets of curiosity. As we wouldhope, the agent exhibits short-term directed behaviour while updating long-termpreferences to adaptively seek out curiosity-inducing situations. This worktherefore presents a novel view into how specific curiosity operates and in thefuture might be integrated into the behaviour of goal-seeking, decision-makingagents in complex environments.

Prototyping three key properties of specific curiosity in computational reinforcement learning