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. The methodological framework centers on RL principles, employing a Q-learning agent class and distinct environment classes tailored to each spatial dimension. The research aims to address the question: How do reinforcement learning agents adapt and perform in environments of varying spatial dimensions, particularly in 2D and 3D settings? Through empirical analysis, the study evaluates agents' learning trajectories and adaptation processes, revealing insights into the efficacy of RL algorithms in navigating complex, multi-dimensional spaces. Reflections on the findings prompt considerations for future research, particularly in understanding the dynamics of learning in higher-dimensional environments.
Abstract (translated)
强化学习（RL）算法已成为人工智能的不可或缺的工具，通过与环境的交互和反馈机制，使智能体获得最优决策策略。本研究探讨了在二维（2D）和三维（3D）环境中RL代理器的性能，旨在研究不同空间维度下的学习动态。这一研究的关键点是缺乏为学习预先制定的库，该算法是通过计算数学仅通过计算得出的。方法论框架以RL原则为核心，采用针对每个空间维度的不同环境类和Q学习智能体类。研究的目的是回答这个问题：强化学习智能体如何在具有不同空间维度的环境中进行适应和表现？通过实证分析，研究评估了智能体的学习轨迹和适应过程，揭示了RL算法在复杂多维空间中导航的有效性。对研究结果的反思引发了关于未来研究的考虑，特别是在理解高维环境中学习动态方面。
URL
https://arxiv.org/abs/2403.18219
