From Two-Dimensional to Three-Dimensional Environment with Q-Learning: Modeling Autonomous Navigation with Reinforcement Learning and no Libraries

arXiv:2403.18219v1 Announce Type: new
Abstract: Reinforcement learning (RL) algorithms have become indispensable tools in artificial intelligence, empowering agents to acquire optimal decision-making policies through interactions with their environment and feedback mechanisms. This study explores the performance of RL agents in both two-dimensional (2D) and three-dimensional (3D) environments, aiming to research the dynamics of learning across different spatial dimensions. A key aspect of this investigation is the absence of pre-made libraries for learning, with the algorithm developed exclusively through computational mathematics. …

abstract agents algorithms artificial artificial intelligence arxiv autonomous become cs.ai cs.lg decision environment feedback intelligence interactions libraries making modeling navigation performance policies q-learning reinforcement reinforcement learning stat.co study three-dimensional through tools type