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Reinforcement Learning agents are expected to eventually perform well.Typically, this takes the form of a guarantee about the asymptotic behavior ofan algorithm given some assumptions about the environment. We present analgorithm for a policy whose value approaches the optimal value withprobability 1 in all computable probabilistic environments, provided the agenthas a bounded horizon. This is known as strong asymptotic optimality, and itwas previously unknown whether it was possible for a policy to be stronglyasymptotically optimal in the class of all computable probabilisticenvironments. Our agent, Inquisitive Reinforcement Learner (Inq), is morelikely to explore the more it expects an exploratory action to reduce itsuncertainty about which environment it is in, hence the term inquisitive.Exploring inquisitively is a strategy that can be applied generally; for moremanageable environment classes, inquisitiveness is tractable. We conductedexperiments in "grid-worlds" to compare the Inquisitive Reinforcement Learnerto other weakly asymptotically optimal agents.

A Strongly Asymptotically Optimal Agent in General Environments