Psychlab is a simulated psychology laboratory inside the first-person 3D game
world of DeepMind Lab (Beattie et al. 2016). Psychlab enables implementations
of classical laboratory psychological experiments so that they work with both
human and artificial agents. Psychlab has a simple and flexible API that
enables users to easily create their own tasks. As examples, we are releasing
Psychlab implementations of several classical experimental paradigms including
visual search, change detection, random dot motion discrimination, and multiple
object tracking. We also contribute a study of the visual psychophysics of a
specific state-of-the-art deep reinforcement learning agent: UNREAL (Jaderberg
et al. 2016). This study leads to the surprising conclusion that UNREAL learns
more quickly about larger target stimuli than it does about smaller stimuli. In
turn, this insight motivates a specific improvement in the form of a simple
model of foveal vision that turns out to significantly boost UNREAL's
performance, both on Psychlab tasks, and on standard DeepMind Lab tasks. By
open-sourcing Psychlab we hope to facilitate a range of future such studies
that simultaneously advance deep reinforcement learning and improve its links
with cognitive science.

Psychlab 是 DeepMind Lab 中的模拟心理学实验室，可用于实现经典实验范式和深度强化学习代理的视觉心理物理研究。实验结果表明，UNREAL 的学习速度受目标刺激大小的影响，并通过提供简单的视觉准则来改善 UNREAL 在任务中的表现。