Reinforcement Learning: Machines Learning from Experience

Reinforcement learning (RL) is an advanced machine learning technique where an agent learns to make decisions by interacting with an environment. Instead of using labeled data, RL models rely on rewards and penalties to guide learning.

Key concepts in reinforcement learning include:

• Agent: The learner or decision-maker in the RL system.
• Environment: The world in which the agent operates.
• Actions: The possible choices the agent can make.
• Reward Function: A system that provides feedback to the agent based on its actions.
• Q-Learning: A popular reinforcement learning algorithm that helps agents optimize their decisions over time.

Reinforcement learning is widely used in robotics, gaming (e.g., AlphaGo), autonomous driving, and dynamic pricing strategies.