Reinforcement Learning: From Q-Learning to Deep Policy Gradients

Reinforcement learning is the driving force behind modern decision-making AI, from game-playing agents to autonomous systems. Understanding how agents learn through trial and error is crucial for anyone entering the field of advanced artificial intelligence. This text-based course guides you from the absolute basics of decision-making frameworks to implementing powerful deep reinforcement learning algorithms. You will learn how to model environments, define rewards, and train agents that can adapt and optimize their behavior over time. What you'll learn: - Understand the core mathematical foundations of Markov Decision Processes and reward structures - Implement classic tabular Q-learning algorithms to solve grid-world decision problems - Transition to deep reinforcement learning by building Deep Q-Networks with neural networks - Apply policy gradient methods including REINFORCE and understand actor-critic architectures - Configure standardized environments using the modern Gymnasium API for training agents - Explore contemporary applications of reinforcement learning, including the concepts behind RLHF We begin with essential terminology, state-action-reward loops, and dynamic programming. From there, you will progress through step-by-step written explanations and code implementations of both value-based and policy-based deep learning methods. This course is designed for beginners in machine learning who want to specialize in reinforcement learning. A basic familiarity with Python and neural network concepts is recommended, but no prior reinforcement learning experience is required. Start reading today to master the algorithms that power modern adaptive AI.