Reinforcement Learning: Foundations of Goal-Oriented Intelligence

Description

Course Curriculum

Module 1: Introduction to Reinforcement Learning Series – Syllabus Overview

  • Introduction to Reinforcement Learning Series – Syllabus Overview
    00:00

Module 2: Deep Q-Network Code Project Introduction – Reinforcement Learning

Module 3: Deep Q-Learning – Integrating Neural Networks with Reinforcement Learning

Module 4: Deep Q-Network Training Implementation – Reinforcement Learning Code Project

Module 5: Deep Q-Network Image Processing and Environment Management – Reinforcement Learning Code Project

Module 6: Building a Deep Q-Network – Reinforcement Learning Code Project

Module 7: Policies and Value Functions – Choosing Optimal Actions for a Reinforcement Learning Agent

Module 8: OpenAI Gym and Python for Q-Learning – Reinforcement Learning Code Project

Module 9: Markov Decision Processes (MDPs) – Structuring a Reinforcement Learning Problem

Module 10: Exploration vs Exploitation – Learning the Optimal Reinforcement Learning Policy

Module 11: Expected Return – What Drives a Reinforcement Learning Agent in an MDP

Module 12: Training a Deep Q-Network – Reinforcement Learning

Module 13: Training a Deep Q-Network with Fixed Q-Targets – Reinforcement Learning

Module 14: Replay Memory Explained – Experience for Deep Q-Network Training

Module 15: Q-Learning Explained – A Reinforcement Learning Technique

Module 16: Training a Q-Learning Agent with Python – Reinforcement Learning Code Project

Module 17: Watching a Q-Learning Agent Play a Game with Python – Reinforcement Learning Code Project

Module 18: What Reinforcement Learning Algorithms Learn – Optimal Policies

$14.00 $114.00
  • Level
    Intermediate
  • Last Updated
    June 4, 2026