Dive into deep reinforcement learning
We humans learn through trial and error to take better actions based on the rewards and punishments we receive. Over time, we optimize our actions by maximizing the rewards and minimizing punishment. This process is called Reinforcement Learning (RL). This technique is used in various domains like robotics and AI, where agents learn to make decisions by interacting with the environment and receiving feedback.
In this section, we will discuss important concepts in RL and DRL. This will give you a quick guide before we jump into the application side of things. If you are new to RL, this section will be very useful to you. So, let’s begin by discussing RL and DRL.
Basic concepts of reinforcement learning
Reinforcement learning is a type of machine learning in which the agent learns from interacting with the environment. An agent performs actions and receives rewards or penalties. Based on the rewards and penalties, the agent adjusts...