Deep Reinforcement Learning: Techniques for training agents to make sequential decisions in complex environments.
Wasif Ali · 2024
Deep reinforcement learning (DRL) is a powerful machine learning technique that allows agents to learn how to make decisions in complex environments by trial and error. DRL combines reinforcement learning (RL) with deep learning, which allows agents to learn from large amounts of data and make decisions based on complex features. In this paper, we review the techniques used in DRL, and we discuss the challenges and opportunities of this field. We also present some examples of how DRL is being used today, and we discuss the future of DRL.