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

• 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
  

• 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.
  

• 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.
  

• Python (with full code walkthroughs)
  
• Gymnasium (formerly OpenAI Gym)
  
• Stable Baselines 3
  
• NumPy, Matplotlib, PyTorch (where applicable)
  

• 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.
  

• 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
  

• 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