Name
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. |
Year | Venue | DocType |
|---|---|---|
2019 | arXiv: Artificial Intelligence | Journal |
Volume | Citations | PageRank |
abs/1905.04640 | 0 | 0.34 |
References | Authors | |
0 | 6 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yuhang Song | 1 | 17 | 6.06 |
| Jianyi Wang | 2 | 17 | 3.69 |
| Thomas Lukasiewicz | 3 | 2618 | 165.18 |
| Zhenghua Xu | 4 | 27 | 6.77 |
| Shangtong Zhang | 5 | 9 | 8.63 |
| Mai Xu | 6 | 509 | 57.90 |