Smart homes and self-driving vehicles increasingly use sensor networks in the IoT era. Managing large datasets and autonomous decision-making is challenging. Reinforcement learning (RL) offers a solution. This study explores RL-based autonomous decision-making in sensor systems, discussing principles, challenges, and opportunities. The proposed method combines Deep Q-Networks, Proximal Policy Optimization, and Actor-Critic algorithms to improve decision-making speed, accuracy, and learning efficiency. RL is effective in safety-critical tasks, such as self-driving cars, though resource demands and reliability remain concerns. © 2024 Elsevier B.V., All rights reserved.