Proposed reinforcement learning architecture.

<div><p>In the realm of game playing, deep reinforcement learning predominantly relies on visual input to map states to actions. The visual data extracted from the game environment serves as the primary foundation for state representation in reinforcement learning agents. However, humans...

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Bibliographic Details
Main Author:	Enoch Solomon (21416703) (author)
Published:	2025
Subjects:	Neuroscience Sociology Science Policy Mental Health Space Science Biological Sciences not elsewhere classified ultimately enhancing performance proximal policy optimization learning rate compared using raw audio raw audio samples particularly raw audio audio features enhances incorporating multisensory information offering valuable insights game environment serves algorithms within vizdoom reinforcement learning agents agent &# 8217 augmenting visual information visual data extracted audio cues visual data complementary information insights contribute game playing autonomous agents agents perceive visual input visual cues xlink "> vizdoom environments study underscores study advocates state representation relying solely primary foundation potential advantages pivotal role map states findings suggest experiments reveal experimental evaluation
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Proposed reinforcement learning architecture.

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