This paper describes a unique approach to object detection that combines optical sensors with reinforcement learning algorithms for a collaborative robot. In this paper, we offer a resilient and adaptive system that uses reinforcement learning to improve object detection performance. The framework is intended to optimize decision-making and increase object identification. The primary objective of this research is to create and integrate a deep reinforcement learning model that successfully learns to make educated judgments based on optical sensor inputs. The model gradually adjusts to different ambient conditions, noise, and complicated scenarios through a series of trials and iterative learning, resulting in a more comprehensive and adaptive object identification system. Optimize decision-making and enhance object identification accuracy, resulting in more efficient and reliable detection in real-world applications. The results show that the reinforcement learning-based object identification system outperforms traditional detection approaches. Research explores framework mechanisms, revealing sensor-reinforcement learning interplay (Deep Q Networks). Integration signifies a significant object detection advancement. Outcomes demonstrate the technique's utility, and transformative potential in diverse fields like autonomous cars, surveillance, and robotics. Future research can build upon these findings for further development.