Reinforcement Learning

Here, you’ll find everything related to reinforcement learning.

Reinforcement learning is a type of machine learning in which an agent learns to take actions in an environment to maximize a reward. The agent receives feedback in the form of rewards or punishments for its actions, and uses this feedback to adjust its behavior. The goal is for the agent to learn a policy that will maximize the cumulative reward over time. Reinforcement learning can be used to solve a variety of tasks, including control problems, optimization, and games. Algorithms often use a combination of trial and error and learning from past experience to improve their performance.