@Article{jai.2025.073535,
AUTHOR = {Miah A. Robinson, Abdulghani M. Abdulghani, Mokhles M. Abdulghani, Khalid H. Abed},
TITLE = {Improving the Performance of AI Agents for Safe Environmental Navigation},
JOURNAL = {Journal on Artificial Intelligence},
VOLUME = {7},
YEAR = {2025},
NUMBER = {1},
PAGES = {615--632},
URL = {http://www.techscience.com/jai/v7n1/64686},
ISSN = {2579-003X},
ABSTRACT = {Ensuring the safety of Artificial Intelligence (AI) is essential for providing dependable services, especially in various sectors such as the military, education, healthcare, and automotive industries. A highly effective method to boost the precision and performance of an AI agent involves multi-configuration training, followed by thorough evaluation in a specific setting to gauge performance outcomes. This research thoroughly investigates the design of three AI agents, each configured with a different number of hidden units. The first agent is equipped with 128 hidden units, the second with 256, and the third with 512, all utilizing the Proximal Policy Optimizer (PPO) algorithm. Importantly, all agents are trained in a uniform environment using the Unity simulation platform, employing the Machine Learning Agents Toolkit (ML-agents) in conjunction with the PPO algorithm enhanced by an Intrinsic Curiosity Module (PPO + ICM). The main aim of this study is to clearly highlight the benefits and limitations of increasing the number of hidden units. The results convincingly show that expanding the hidden units to 512 leads to a notable 50% enhancement in the agent’s Goal (G) and a substantial 50% decrease in the Collision (C) value. This study offers a detailed analysis of how the number of hidden units affects AI agent performance using the Proximal Policy Optimizer (PPO) algorithm, augmented with an Intrinsic Curiosity Module (ICM). By systematically comparing agents with 128, 256, and 512 hidden units in a controlled Unity environment, the research provides valuable insights into the connection between network complexity and task performance. The consistent use of the ML-Agents Toolkit ensures a standardized training process, facilitating direct comparisons between the different configurations.},
DOI = {10.32604/jai.2025.073535}
}