Reinforcement Learning Specialization Course Syllabus

Overview: This specialization provides a comprehensive introduction to reinforcement learning, designed for learners aiming to master core concepts and practical applications. The course is divided into four main modules, each lasting approximately 4 weeks, with a recommended commitment of 10 hours per week. Learners will progress from foundational theories to building complete RL systems through hands-on projects. Total time commitment is approximately 160 hours.

Module 1: Fundamentals of Reinforcement Learning

Estimated time: 40 hours

• Introduction to reinforcement learning and its applications
• Markov Decision Processes (MDPs)
• Value functions and policy evaluation
• Dynamic programming methods for solving MDPs

Module 2: Sample-based Learning Methods

Estimated time: 40 hours

• Monte Carlo methods for prediction and control
• Temporal-Difference learning (TD-learning)
• Sarsa and on-policy learning
• Q-learning and off-policy learning

Module 3: Prediction and Control with Function Approximation

Estimated time: 40 hours

• Introduction to function approximation in RL
• Linear function approximators
• Neural networks for value function approximation
• Deep Q-Networks (DQN) and control with approximation

Module 4: Dyna and Integrated Planning

Estimated time: 40 hours

• Model-based vs. model-free methods
• Planning with learned models
• Dyna architecture: combining learning and planning
• Integration of real experience and simulated experience

Module 5: Policy Gradients and Advanced Methods

Estimated time: 40 hours

• Introduction to policy gradient methods
• REINFORCE algorithm
• Actor-Critic architectures
• Combining policy gradients with function approximation

Module 6: A Complete Reinforcement Learning System (Capstone)

Estimated time: 40 hours

• Formalize a real-world problem as an RL task
• Implement a full RL solution in Python
• Evaluate and optimize performance using best practices

Prerequisites

• Familiarity with Python programming
• Basic understanding of machine learning concepts
• Knowledge of probability and linear algebra recommended

What You'll Be Able to Do After

• Understand and apply core reinforcement learning algorithms
• Formalize real-world decision-making problems as RL tasks
• Implement and train RL agents using Python and function approximation
• Build complete RL systems integrating learning, planning, and control
• Advance into roles such as Machine Learning Engineer or AI Specialist