Reinforcement Learning: Build Practical AI Agents from Scratch

Reinforcement learning is the driving force behind self-driving cars, game-playing AIs, and adaptive robotics. To truly understand how these autonomous systems learn from their environments, you need to build them yourself. This text-based course guides you from foundational AI concepts to implementing your own reinforcement learning agents. You will read clear explanations of complex algorithms and write clean, modern Python code to solve classic control and decision-making problems. What you'll learn: Understand core reinforcement learning concepts, including Markov Decision Processes, rewards, and policy iteration; Build classic Q-learning algorithms from scratch using standard Python libraries; Implement deep reinforcement learning concepts using modern neural network frameworks; Configure and interact with environment simulators using updated Gymnasium standards; Apply policy gradient methods to solve continuous control problems; Analyze and optimize agent performance using modern training workflows. The course begins with foundational terminology, defining agents, environments, and state-action spaces. You will then progress step-by-step through value-based methods, policy-based approaches, and modern deep reinforcement learning techniques. Designed for beginner developers and aspiring AI engineers who have a basic understanding of Python and want to learn reinforcement learning without complex prerequisites. Start reading today to build your first intelligent, self-learning agent.