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
The continuous drive to enhance AI learning efficiency in complex simulated and real-world environments propels research into novel input representations like semantic segmentation.
Improving reinforcement learning's efficiency and robustness in 3D environments, especially with high-dimensional sensory input, is crucial for developing more capable and autonomous AI agents and embodied AI.
This research suggests a more effective way to process visual information for RL, potentially reducing computational demands and improving learning stability in complex tasks.
- · AI agents developers
- · Robotics research
- · Gaming AI
- · Autonomous systems
- · Computational resource-intensive RL methods
- · Less efficient 3D environment simulation
More efficient training of AI models for tasks in complex visual environments.
Accelerated development of AI agents capable of performing sophisticated tasks in physical and virtual 3D spaces with reduced computational overhead.
Potentially lowers the barrier to entry for developing advanced AI, leading to broader application in fields like robotic process automation, virtual reality, and complex system control.
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Read at arXiv cs.LG