Reinforcement Learning

Guides to reinforcement learning: agents, environments, rewards, policies, algorithms, and how RL compares with other machine learning approaches.

What Is an Agent in Reinforcement Learning?

Muhammad Waseem / November 6, 2026

What is an agent in reinforcement learning? Learn how agents observe states, choose actions, follow policies, and learn from rewards, with a maze example and runnable Python code.

Reinforcement vs unsupervised learning: an agent learning from rewards versus grouping unlabeled data

Reinforcement Learning vs Unsupervised Learning

Muhammad Waseem / November 6, 2026

Reinforcement learning vs unsupervised learning: compare objectives, feedback, data, algorithms like K-Means and Q-learning, examples, and when to use each approach.

Machine Learning, Reinforcement Learning

Reinforcement Learning vs Supervised Learning

Muhammad Waseem / November 6, 2026

Compare reinforcement learning vs supervised learning: how each learns, labels vs rewards, a comparison table, hybrid methods, and when to choose each.

How reinforcement learning works: observe the state, choose an action, receive a reward, update the policy

Machine Learning, Reinforcement Learning

How Does Reinforcement Learning Work?

Muhammad Waseem / November 6, 2026

Learn how reinforcement learning works step by step: the agent-environment loop, rewards, exploration, the Bellman equation, and a worked maze example with real numbers.

What is reinforcement learning: an agent takes actions in an environment and receives a state and reward

Machine Learning, Reinforcement Learning

What Is Reinforcement Learning? A Complete Guide

Muhammad Waseem / November 6, 2026

Reinforcement learning (RL) trains an agent to make decisions by interacting with an environment and maximizing reward. Learn the core components, main algorithms, applications, and how to start.