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

Build Artificial Intelligence (AI) agents using Reinforcement Learning in PyTorch: DQN, A2C, Policy Gradients, +More!

What you'll learn

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[*]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
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Requirements

[list]
[*]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.
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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

[list]
[*]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.
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Tools and Libraries

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[*]Python (with full code walkthroughs)
[*]Gymnasium (formerly OpenAI Gym)
[*]Stable Baselines 3
[*]NumPy, Matplotlib, PyTorch (where applicable)
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Why This Course?

[list]
[*]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.
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Who Should Take This Course?

[list]
[*]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
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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:

[list]
[*]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
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Homepage

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