Policy Gradients with the REINFORCE Algorithm in Python

Reinforcement learning can feel intimidating with its complex math, but policy gradients offer an elegant, direct way to train intelligent agents. Understanding the foundational REINFORCE algorithm is the key to unlocking modern deep reinforcement learning. This text-only course guides you through the core theory and practical implementation of the REINFORCE algorithm in Python. You will transition from understanding basic probability-based decision-making to writing clean, modern PyTorch code that solves classic control problems. What you'll learn: 1. Understand the mathematical foundations of policy gradients and the policy objective function. 2. Implement the REINFORCE algorithm from scratch using clean, modern Python and PyTorch. 3. Apply trajectory collection and discounted reward calculations to update policy networks. 4. Configure and debug neural network policies using type hints and modern code conventions. 5. Practice troubleshooting common training issues like high variance and slow convergence. 6. Analyze agent performance across different environment setups through structured written exercises. The course begins with essential terminology, probability concepts, and reinforcement learning foundations. You will then progress step-by-step through the derivation of the policy gradient theorem, followed by hands-on code walkthroughs where you construct the agent, collect trajectories, and optimize the policy. This course is designed for programmers, data science enthusiasts, and students new to reinforcement learning who want a solid, code-first introduction to policy gradients. Basic familiarity with Python and neural network concepts is helpful, but no prior reinforcement learning experience is required. Start reading today to build your first policy-based agent from the ground up.