Reinforcement Learning and Decision Making

Abstract

The study of reinforcement learning is a machine learning paradigm in which agents learn to make decisions by interacting with an environment and obtaining rewards for their actions. Agents use trial and error to find optimal rules that maximize cumulative rewards over time. This strategy is well-suited for complex, dynamic contexts where traditional methods may fall short. Key issues include balancing exploration and exploitation, dealing with delayed rewards, and managing huge state and action spaces. Deep reinforcement learning, which blends deep neural networks with reinforcement learning, has shown exceptional success in a variety of applications, demonstrating the potential for intelligent decision-making in a variety of fields. Ongoing research continues to develop algorithms and extend the possibilities of reinforcement learning for future real-world applications.