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Artificial intelligence is commonly defined as the ability to achieve goalsin the world. In the reinforcement learning framework, goals are encoded asreward functions that guide agent behaviour, and the sum of observed rewardsprovide a notion of progress. However, some domains have no such reward signal,or have a reward signal so sparse as to appear absent. Without reward feedback,agent behaviour is typically random, often dithering aimlessly and lackingintentionality. In this paper we present an algorithm capable of learningpurposeful behaviour in the absence of rewards. The algorithm proceeds byconstructing temporally extended actions (options), through the identificationof purposes that are "just out of reach" of the agent's current behaviour.These purposes establish intrinsic goals for the agent to learn, ultimatelyresulting in a suite of behaviours that encourage the agent to visit differentparts of the state space. Moreover, the approach is particularly suited forsettings where rewards are very sparse, and such behaviours can help in theexploration of the environment until reward is observed.

Learning Purposeful Behaviour in the Absence of Rewards