papers AI Learner
The Github is limit! Click to go to the new site.

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

2019-05-12
Yuhang Song, Jianyi Wang, Thomas Lukasiewicz, Zhenghua Xu, Shangtong Zhang, Mai Xu

Abstract

Intrinsic rewards are introduced to simulate how human intelligence works, which are usually evaluated by intrinsically-motivated play, i.e., playing games without extrinsic rewards but evaluated with extrinsic rewards. However, none of the existing intrinsic reward approaches can achieve human-level performance under this very challenging setting of intrinsically-motivated play. In this work, we propose a novel megalomania-driven intrinsic reward (mega-reward) which, to our knowledge, is the first approach that achieves comparable human-level performance in intrinsically-motivated play. The intuition of mega-rewards comes from the observation that infants’ intelligence develops when they try to gain more control on entities in an environment; therefore, mega-reward aims to maximize the control capabilities of agents on given entities in a given environment. To formalize mega-reward, a relational transition model is proposed to bridge the gaps between direct and latent control. Experimental studies show that mega-reward can (i) greatly outperform all state-of-the-art intrinsic reward approaches, (ii) generally achieves the same level of performance as Ex-PPO and professional human-level scores; and (iii) has also superior performance when it is incorporated with extrinsic reward.

Abstract (translated by Google)
URL

http://arxiv.org/abs/1905.04640

PDF

http://arxiv.org/pdf/1905.04640


Similar Posts

Comments