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Advanced AI: Deep Reinforcement Learning in PyTorch (v2)

Build Artificial Intelligence (AI) agents using Reinforcement Learning in PyTorch: DQN, A2C, Policy Gradients, +More!
4.7 ★★★★★1,942 studentsCreated by Lazy Programmer Inc., Lazy Programmer TeamLast updated Nov 3, 2025🌐 English

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

Frequently Asked Questions

Student Feedback

4.7
★★★★★
Course Rating
75%
15%
5%
5%
5%
S
Sarah J.
★★★★★2 weeks ago

This course was absolutely amazing! The instructor explained everything clearly and the projects were very helpful.

M
Michael T.
★★★★1 month ago

Great content, highly recommended for beginners. Just wish there were more practice exercises.

D
David K.
★★★★★2 months ago

Best course on this topic I've taken so far. Worth every penny (even better since I got it for free!).

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Advanced AI: Deep Reinforcement Learning in PyTorch (v2)
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This course includes:
  • 📺 15h 30m on-demand video
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  • ♾️ Full lifetime access
  • 🏆 Certificate of completion