Autonomous mobile robot navigation integrates localization, mapping, and path planning to enable effective operation in complex environments. This study compares a modified Q-learning algorithm with a Deep Q-Network (DQN) in a simulated gym environment, focusing on convergence speed, success rate, and computational efficiency. The modified Q-learning algorithm converged after 44 episodes, outperforming the DQN, which required 400 episodes. It achieved a success rate of 69.6% with a cumulative reward that surpassed the DQN in fewer episodes, while completing simulations in just 9 minutes compared to 400 minutes for the DQN. These results demonstrate the modified Q-learning’s efficiency in addressing the exploration-exploitation trade-off and navigating complex environments. This study highlights the potential of the modified Q-learning algorithm for real-world applications in robotics and autonomous navigation, providing a foundation for future research in intelligent path planning
