In this video, we're launching a new section and a new course dedicated to Reinforcement Learning.
Reinforcement Learning (RL) is a branch of machine learning where an agent learns to make decisions by interacting with an environment. Unlike supervised learning, where the model learns from labeled examples, RL agents learn through trial and error, receiving rewards or penalties based on their actions.
The agent's goal is to learn a policy that maximizes the accumulated reward over time. This is formalized as maximizing the expected return, often discounted to favor immediate rewards over distant ones. We'll initially look at the types of algorithms available: model-based and model-free, then, we'll examine the variants of Policy Optimization, Q-Learning, and Hybrid Models.
Code
https://github.com/olonok69/Introduction_to_Reinforcement_Learning/blob/main/README.md
