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Intrinsic rewards were introduced to simulate how human intelligence works;they are usually evaluated by intrinsically-motivated play, i.e., playing gameswithout extrinsic rewards but evaluated with extrinsic rewards. However, noneof the existing intrinsic reward approaches can achieve human-level performanceunder this very challenging setting of intrinsically-motivated play. In thiswork, we propose a novel megalomania-driven intrinsic reward (calledmega-reward), which, to our knowledge, is the first approach that achieveshuman-level performance in intrinsically-motivated play. Intuitively,mega-reward comes from the observation that infants' intelligence develops whenthey try to gain more control on entities in an environment; therefore,mega-reward aims to maximize the control capabilities of agents on givenentities in a given environment. To formalize mega-reward, a relationaltransition model is proposed to bridge the gaps between direct and latentcontrol. Experimental studies show that mega-reward (i) can greatly outperformall state-of-the-art intrinsic reward approaches, (ii) generally achieves thesame level of performance as Ex-PPO and professional human-level scores, and(iii) has also a superior performance when it is incorporated with extrinsicrewards.

Mega-Reward: Achieving Human-Level Play without Extrinsic Rewards