Section: Deep Reinforcement Learning | Advanced Deep Learning

Section outline

- Select activity Deep_Reinforcement_Learning
  
  Deep_Reinforcement_Learning File
  
  In this chapter, we delve into the concept of Deep Reinforcement Learning (DRL). DRL merges reinforcement learning (RL) with deep learning, enabling agents to make decisions and learn policies for complex tasks. Unlike traditional RL, which relies on hand-crafted features, DRL uses neural networks to automatically learn representations. This allows agents to handle high-dimensional state spaces, such as those found in video games or robotic control. Through the combination of deep neural networks and reward-based learning, DRL has achieved remarkable successes, including mastering games like Go and complex robotic manipulations.