Reinforcement Learning

Reinforcement Learning (RL) is a type of machine learning where an agent learns to make decisions by interacting with an environment. The agent receives rewards or penalties based on its actions, and its goal is to maximize the cumulative reward over time.

The key components of RL include:

Agent: The learner or decision-maker.
Environment: The external system with which the agent interacts.
Actions: The choices the agent can make.
Rewards: Feedback signals indicating the success of an action.
The learning process in RL is often modeled using Markov Decision Processes (MDPs). Algorithms like Q-Learning and Deep Q-Networks (DQN) are popular in reinforcement learning.

RL is widely used in robotics, game-playing (like AlphaGo), autonomous vehicles, and dynamic pricing strategies.