Enhancing Reinforcement Learning in 3D Environments through Semantic Segmentation: A Case Study in ViZDoom

arXiv:2511.11703v2 Announce Type: replace Abstract: Reinforcement learning (RL) in 3D environments with high-dimensional sensory input poses two major challenges: (1) the high memory consumption induced by memory buffers required to stabilise learning, and (2) the complexity of learning in partially observable Markov Decision Processes (POMDPs). This project addresses these challenges by proposing two novel input representations: SS-only and RGB+SS, both employing semantic segmentation on RGB colour images. Experiments were conducted in deathmatches of ViZDoom, utilizing perfect segmentation r

Source: arXiv cs.LG — read the full report at the original publisher.

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