Udemy – Advanced AI: Deep Reinforcement Learning in PyTorch (v2) 2025-5

Udemy – Advanced AI: Deep Reinforcement Learning in PyTorch (v2) 2025-5 Downloadly IRSpace

Aug 16, 2025 - 07:35 Updated: Aug 17, 2025 - 20:12

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Udemy – Advanced AI: Deep Reinforcement Learning in PyTorch (v2) 2025-5

Advanced AI: Deep Reinforcement Learning in PyTorch (v2) course. This comprehensive and up-to-date course covers basic to advanced concepts of reinforcement learning (RL) and is designed for students, researchers, and AI engineers. Participants will learn RL fundamentals such as rewards, value functions, the Bellman equation, and Markov decision processes (MDPs), and will implement key algorithms such as Q-Learning, temporal differential learning (TD), and Monte Carlo methods in Python and the Gymnasium library. The course will also teach you how to build Deep Q-Networks (DQN) using techniques such as experience replay and goal networks, and explore advanced methods such as Policy Gradient and A2C. One of the highlights is implementing AI for Atari games with the Stable Baselines 3 library. Additionally, topics such as policy optimization, entropy regularization, and practical real-world applications of RL are covered. With up-to-date content, clear explanations, and practical examples, this course equips participants with the skills needed to build intelligent, adaptive agents.

What you will learn

Review of the basics of reinforcement learning: MDPs, Bellman equation, Q-Learning.
Theory and implementation of Q/DQN deep learning.
Theory and implementation of policy gradient and A2C (Advantage Actor-Critic) methods.
Application of DQN and A2C in Atari environments (e.g. Breakout, Pong, Asteroids).
Using A2C to build a trading algorithm for multi-period portfolio optimization (VIP members only).

This course is suitable for people who:

Machine learning and artificial intelligence enthusiasts who want to explore one of the most exciting areas of artificial intelligence, reinforcement learning.
Software developers and engineers looking to build intelligent agents that learn from experience.
Few financial professionals are interested in using RL in portfolio optimization and algorithmic trading.
Students and researchers in artificial intelligence, computer science, or data science who want to gain hands-on experience with real-world RL implementations.
Game developers who are curious about using RL to train AI for complex behaviors and adaptive gameplay.
Robotics specialists who want to learn how agents can make sequential decisions in physical environments.
Data scientists who want to expand their toolbox beyond supervised learning and unsupervised learning.
Traders and investors looking to use advanced AI methods in automated trading strategies.
Entrepreneurs and enthusiasts who are eager to experiment with advanced AI models and build projects that learn and adapt over time.
Professionals who are transitioning into AI/machine learning and are looking for real, presentable projects to add to their resume.

Advanced AI: Deep Reinforcement Learning in PyTorch (v2) Course Details

Publisher: Udemy
Instructor: Lazy Programmer Inc. , Lazy Programmer Team
Training level: Beginner to advanced
Training duration: 15 hours and 35 minutes
Number of lessons: 65

Course syllabus in 2025/6

Prerequisites for the Advanced AI: Deep Reinforcement Learning in PyTorch (v2) course

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.

Udemy – Advanced AI: Deep Reinforcement Learning in PyTorch (v2) 2025-5