  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.
