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Investigating Human Priors for Playing Video Games

3 Pith papers cite this work. Polarity classification is still indexing.

3 Pith papers citing it
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 https://rach0012.github.io/humanRL_website/

years

2025 1 2019 2

verdicts

UNVERDICTED 3

representative citing papers

Semantic knowledge guides innovation and drives cultural evolution

cs.MA · 2025-10-13 · unverdicted · novelty 6.0

Semantic knowledge directs exploration to meaningful innovations, improves success rates, enables generalization, and synergizes with social learning to accelerate cumulative culture, as tested in agent-based models and a large behavioral experiment.

Fooling a Real Car with Adversarial Traffic Signs

cs.CR · 2019-06-30 · unverdicted · novelty 6.0

A reproducible pipeline produces physical adversarial traffic signs that successfully attack production-grade traffic sign recognition systems in a real car under black-box conditions.

citing papers explorer

Showing 3 of 3 citing papers.

  • Semantic knowledge guides innovation and drives cultural evolution cs.MA · 2025-10-13 · unverdicted · none · ref 33 · internal anchor

    Semantic knowledge directs exploration to meaningful innovations, improves success rates, enables generalization, and synergizes with social learning to accelerate cumulative culture, as tested in agent-based models and a large behavioral experiment.

  • Fooling a Real Car with Adversarial Traffic Signs cs.CR · 2019-06-30 · unverdicted · none · ref 46 · internal anchor

    A reproducible pipeline produces physical adversarial traffic signs that successfully attack production-grade traffic sign recognition systems in a real car under black-box conditions.

  • On Inductive Biases in Deep Reinforcement Learning cs.LG · 2019-07-05 · unverdicted · none · ref 5 · internal anchor

    Adaptive replacements for domain-specific components in deep RL agents can yield better learning on new tasks without additional tuning.