Investigating Human Priors for Playing Video GamesInvestigating Human Priors for Playing Video GamesRachit Dubey and Pulkit Agrawal and Deepak Pathak and Thomas L. Griffiths and Alexei A. Efros2018
Paper summaryfabioperezAuthors investigated why humans play some video games better than machines. That is the case for games that do not have continuous rewards (e.g., scores). They experimented with a game -- inspired by _Montezuma's Revenge_ -- in which the player has to climb stairs, collect keys and jump over enemies. RL algorithms can only know if they succeed if they finish the game, as there is no rewards during the gameplay, so they tend to do much worse than humans in these games.
To compare between humans and machines, they set up RL algorithms and recruite players from Amazon Mechanical Turk. Humans did much better than machines for the original game setup. However, authors wanted to check the impact of semantics and prior knowledge on humans performance. They set up scenarios with different levels of reduced semantic information, as shown in Figure 2.
This is what the game originally looked like:
And this is the version with lesser semantic clues:
You can try yourself in the [paper's website](https://rach0012.github.io/humanRL_website/).
Not surprisingly, humans took much more time to complete the game in scenarios with less semantic information, indicating that humans strongly rely on prior knowledge to play video games.
The authors argue that this prior knowledge should also be somehow included into RL algorithms in order to move their efficiency towards the human level.
## Additional reading
[Why humans learn faster than AI—for now](https://www.technologyreview.com/s/610434/why-humans-learn-faster-than-ai-for-now/).
Investigating Human Priors for Playing Video Games
Thomas L. Griffiths
Alexei A. Efros
arXiv e-Print archive - 2018 via Local arXiv
First published: 2018/02/28 (1 year ago) Abstract: What makes humans so good at solving seemingly complex video games? Unlike
computers, humans bring in a great deal of prior knowledge about the world,
enabling efficient decision making. This paper investigates the role of human
priors for solving video games. Given a sample game, we conduct a series of
ablation studies to quantify the importance of various priors on human
performance. We do this by modifying the video game environment to
systematically mask different types of visual information that could be used by
humans as priors. We find that removal of some prior knowledge causes a drastic
degradation in the speed with which human players solve the game, e.g. from 2
minutes to over 20 minutes. Furthermore, our results indicate that general
priors, such as the importance of objects and visual consistency, are critical
for efficient game-play. Videos and the game manipulations are available at