Advanced AI: Deep Reinforcement Learning in PyTorch (v2)
Advanced AI: Deep Reinforcement Learning in PyTorch (v2)
.MP4, AVC, 1280x720, 30 fps | English, AAC, 2 Ch | 15h 35m | 5.66 GB
Created by Lazy Programmer Team
.MP4, AVC, 1280x720, 30 fps | English, AAC, 2 Ch | 15h 35m | 5.66 GB
Created by Lazy Programmer Team
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 Learn
- Core 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 Libraries
- Python (with full code walkthroughs)
- Gymnasium (formerly OpenAI Gym)
- Stable Baselines 3
- NumPy, 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 Learning
- Students and Researchers looking to apply RL in academic or practical projects
- Developers who want to build intelligent agents or AI-powered games
- Anyone fascinated by how machines can learn through interaction
Join 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!
Who this course is for:
- Machine Learning & AI enthusiasts who want to explore one of the most exciting fields in AI: reinforcement learning
- Software developers and engineers looking to build intelligent agents that learn from experience
- Quantitative finance professionals interested in applying RL to portfolio optimization and algorithmic trading
- Students and researchers studying AI, computer science, or data science who want hands-on experience with real RL implementations
- Game developers curious about using RL to train AI for complex behaviors and adaptive gameplay
- Robotics practitioners who want to learn how agents can make sequential decisions in physical environments
- Data scientists aiming to expand their toolkit beyond supervised learning / unsupervised learning
- Traders and investors looking to apply cutting-edge AI methods to automated trading strategies
- Entrepreneurs and hobbyists eager to experiment with advanced AI models and build projects that learn and adapt over time
- Professionals switching careers into AI/ML and looking for portfolio-ready, real-world projects
Advanced AI: Deep Reinforcement Learning in PyTorch (v2)
