Reinforcement Learning Techniques for Autonomous Decision-Making Systems

Abstract

One important paradigm for empowering intelligent systems to make decisions on their own is reinforcement learning. Agents that use reinforcement learning to learn from their surroundings can modify their actions in response to positive and negative reinforcement. Where there are no fixed rules, such as in a dynamic and unpredictable environment, this learning architecture excels. Reinforcement learning methods, with an emphasis on deep and classical RL, implemented in autonomous decision-making systems. Within the framework of robotics, autonomous vehicles, resource management, and control systems, methods including deep Q-networks, policy gradient algorithms, and Q-learning are examined. how these methods allow for agents to maximize long-term goals while reacting to changes in their environment in real-time. When contrasted with conventional control strategies, systems based on reinforcement learning show remarkable robustness and adaptability. The efficiency, safety, and scalability of samples, however, continue to be major obstacles. In order to guarantee the secure implementation of reinforcement learning in autonomous systems, the study finishes by stressing the importance of trustworthy training methods and ethical concerns.