  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".
