Reinforcement Learning (RL) has achieved significant milestones in the gaming
domain, most notably Google DeepMind's AlphaGo defeating human Go champion Ken
Jie. This victory was also made possible through the Atari Learning Environment
(ALE): The ALE has been foundational in RL research, facilitating significant
RL algorithm developments such as AlphaGo and others. In current Atari video
game RL research, RL agents' perceptions of its environment is based on raw
pixel data from the Atari video game screen with minimal image preprocessing.
Contrarily, cutting-edge ML research, external to the Atari video game RL
research domain, is focusing on enhancing image perception. A notable example
is Meta Research's "Segment Anything Model" (SAM), a foundation model capable
of segmenting images without prior training (zero-shot). This paper addresses a
novel methodical question: Can state-of-the-art image segmentation models such
as SAM improve the performance of RL agents playing Atari video games? The
results suggest that SAM can serve as a "virtual augmented reality" for the RL
agent, boosting its Atari video game playing performance under certain
conditions. Comparing RL agent performance results from raw and augmented pixel
inputs provides insight into these conditions. Although this paper was limited
by computational constraints, the findings show improved RL agent performance
for augmented pixel inputs and can inform broader research agendas in the
domain of "virtual augmented reality for video game playing RL agents".

这篇论文研究了使用图像分割模型改善 Atari 视频游戏下强化学习代理性能的方法，结果表明当使用增强像素输入时，性能得到提升，这可以为 “虚拟增强现实视频游戏强化学习代理” 领域的更广泛研究提供指导。

虚拟现实增强的 Atari 强化学习

Virtual Augmented Reality for Atari Reinforcement Learning

Large-scale public datasets have been shown to benefit research in multiple
areas of modern artificial intelligence. For decision-making research that
requires human data, high-quality datasets serve as important benchmarks to
facilitate the development of new methods by providing a common reproducible
standard. Many human decision-making tasks require visual attention to obtain
high levels of performance. Therefore, measuring eye movements can provide a
rich source of information about the strategies that humans use to solve
decision-making tasks. Here, we provide a large-scale, high-quality dataset of
human actions with simultaneously recorded eye movements while humans play
Atari video games. The dataset consists of 117 hours of gameplay data from a
diverse set of 20 games, with 8 million action demonstrations and 328 million
gaze samples. We introduce a novel form of gameplay, in which the human plays
in a semi-frame-by-frame manner. This leads to near-optimal game decisions and
game scores that are comparable or better than known human records. We
demonstrate the usefulness of the dataset through two simple applications:
predicting human gaze and imitating human demonstrated actions. The quality of
the data leads to promising results in both tasks. Moreover, using a learned
human gaze model to inform imitation learning leads to an 115\% increase in
game performance. We interpret these results as highlighting the importance of
incorporating human visual attention in models of decision making and
demonstrating the value of the current dataset to the research community. We
hope that the scale and quality of this dataset can provide more opportunities
to researchers in the areas of visual attention, imitation learning, and
reinforcement learning.

收集了 20 种不同 Atari 视频游戏中 117 小时的人类行为与眼动同时记录的大规模高质量数据集，引入了一种新形式的游戏玩法，演示了预测人类注视和模仿人类表现动作的应用，并证明指导学习中加入人类注视行为可以提高游戏性能，这凸显了在决策制定过程中加入人类视觉注意力的重要性与本数据集对视觉注意力、模仿学习和强化学习研究领域的价值。