Published 5/2025
Created by Lazy Programmer Team,Lazy Programmer Inc.
MP4 | Video: h264, 1280×720 | Audio: AAC, 44.1 KHz, 2 Ch
Level: Expert | Genre: eLearning | Language: English | Duration: 63 Lectures ( 15h 35m ) | Size: 5.65 GB
Build Artificial Intelligence (AI) agents using Reinforcement Learning in PyTorch: DQN, A2C, Policy Gradients, +More!
What you’ll learn
Review Reinforcement Learning Basics: MDPs, Bellman Equation, Q-Learning
Theory and Implementation of Deep Q-Learning / DQN
Theory and Implementation of Policy Gradient Methods and A2C (Advantage Actor-Critic)
Apply DQN and A2C to Atari Environments (Breakout, Pong, Asteroids, etc.)
VIP Only: Apply A2C to Build a Trading Algorithm for Multi-Period Portfolio Optimization
Requirements
Reinforcement Learning fundamentals: MDPs, Bellman Equation, Monte Carlo Methods, Temporal Difference Learning
Undergraduate STEM math: calculus, probability, statistics
Python programming and numerical computing (Numpy, Matplotlib, etc.)
Deep Learning fundamentals: Convolutional neural networks, hyperparameter optimization, etc.
Description
Are you ready to unlock the power of Reinforcement Learning (RL) and build intelligent agents that can learn and adapt on their own?Welcome to the most comprehensive, up-to-date, and practical course on Reinforcement Learning, now in its highly improved Version 2! Whether you’re a student, researcher, engineer, or AI enthusiast, this course will guide you from foundational RL concepts to advanced Deep RL implementations — including building agents that can play Atari games using cutting-edge algorithms like DQN and A2C.What You’ll LearnCore RL Concepts: Understand rewards, value functions, the Bellman equation, and Markov Decision Processes (MDPs).Classical Algorithms: Master Q-Learning, TD Learning, and Monte Carlo methods.Hands-On Coding: Implement RL algorithms from scratch using Python and Gymnasium.Deep Q-Networks (DQN): Learn how to build scalable, powerful agents using neural networks, experience replay, and target networks.Policy Gradient & A2C: Dive into advanced policy optimization techniques and learn how actor-critic methods work in practice.Atari Game AI: Use modern libraries like Stable Baselines 3 to train agents that play classic Atari games — from scratch!Bonus Concepts: Explore evolutionary methods, entropy regularization, and performance tuning tips for real-world applications.Tools and LibrariesPython (with full code walkthroughs)Gymnasium (formerly OpenAI Gym)Stable Baselines 3NumPy, Matplotlib, PyTorch (where applicable)Why This Course?Version 2 updates: Streamlined content, clearer explanations, and updated libraries.Real implementations: Go beyond theory by building working agents — no black boxes.For all levels: Includes a dedicated review section for beginners and deep dives for advanced learners.Proven structure: Designed by an experienced instructor who has taught thousands of students to success in AI and machine learning.Who Should Take This Course?Data Scientists and ML Engineers who want to break into Reinforcement LearningStudents and Researchers looking to apply RL in academic or practical projectsDevelopers who want to build intelligent agents or AI-powered gamesAnyone fascinated by how machines can learn through interactionJoin thousands of learners and start mastering Reinforcement Learning today — from theory to full implementations of agents that think, learn, and play.Enroll now and take your AI skills to the next level!
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