Adaptive Q-Learning in Noisy Environments: A Study on Sensor Noise Influence

Abstract

Reinforcement learning, particularly Q-learning, has demonstrated significant potential in autonomous navigation applications. However, the environments of the real world introduce sensor noise, which can impact learning efficiency and decision-making. This study examines the influence of sensor noise on Q-learning performance by simulating an agent navigating an environment with noise. We compare two learning strategies: one with fixed hyperparameters and another with dynamically adjusted hyperparameters. Our results show that high sensor noise degrades learning performance, increasing convergence time and sub-optimal decision-making. However, adapting hyperparameters over time improves resilience to noise by optimizing the balance between exploration and exploitation. These findings highlight the importance of robust learning strategies for autonomous systems under uncertain conditions.