Core Programming AI

Introduction to Reinforcement Learning

1 Lesson

About this course

Introduction to Reinforcement Learning is a specialized artificial intelligence course that introduces students to the principles and algorithms of learning through interaction with dynamic environments. The course covers foundational concepts such as Markov decision processes, value functions, and Q-learning, alongside modern deep reinforcement learning approaches including deep Q-networks (DQN), policy gradients, and actor-critic algorithms. Through hands-on programming exercises using Python frameworks like OpenAI Gym and Stable Baselines, students will implement and train RL agents to solve complex control problems, game playing, and optimization tasks. By the end of the course, students will be equipped to design and deploy autonomous decision-making systems for diverse applications, from robotic control simulations to dynamic resource allocation.

Learning objectives

Understand fundamental reinforcement learning concepts, including Markov decision processes, value functions, and the exploration-exploitation trade-off.

Implement both foundational and advanced RL algorithms, ranging from tabular Q-learning and temporal difference methods to deep Q-networks and policy gradients.

Train, evaluate, and optimize RL agents in simulated environments using industry-standard Python libraries like OpenAI Gym and Stable Baselines.

Design effective reward functions and apply reinforcement learning techniques to solve complex challenges involving continuous action spaces and multi-agent systems.

Develop a comprehensive reinforcement learning solution to address a practical real-world optimization or autonomous control problem.

Introduction to Reinforcement Learning

About this course

Course Outline