Reinforcement learning is a type of machine learning where the algorithm learns to make decisions by interacting with an environment.
The goal of reinforcement learning is to learn a policy that maximizes a cumulative reward signal.
The agent in reinforcement learning receives feedback in the form of rewards or penalties based on its actions.
The agent learns by trial and error, exploring the environment and learning from the feedback it receives.
Reinforcement learning algorithms can be classified into model-based and model-free methods.
One of the challenges of reinforcement learning is the exploration-exploitation tradeoff.
Reinforcement learning has applications in robotics, game playing, and autonomous vehicles.
Reinforcement learning is an active area of research, and new algorithms and techniques are being developed continuously.
An Introduction to Unsupervised Learning with use cases