CS 5325 – Reinforcement Learning
Graduate course, Texas State University, Department of Computer Science, 2025
Enter the world where intelligent agents learn to make choices: Welcome to the Matrix of Reinforcement Learning. Reinforcement Learning (RL) powers some of today’s most advanced AI systems, from game-playing agents like AlphaGo to conversational models like ChatGPT. This graduate-level course introduces the core principles of RL, where intelligent agents learn to make decisions by interacting with their environment to maximize long-term rewards. You’ll explore fundamental topics such as Markov Decision Processes, Q-Learning, and Policy Gradients, before advancing to Deep Reinforcement Learning and modern techniques like Proximal Policy Optimization (PPO), which is part of the same family of methods used to align AI models, such as ChatGPT, with human feedback. The course blends theoretical foundations with hands-on projects using Python, PyTorch, and OpenAI Gym.
