Advanced AI: Deep Reinforcement Learning in PyTorch (v2) — 90% Off Coupon

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

⭐ 4.7 out of 5 Rating (1,942 students) Created by Lazy Programmer Inc., Lazy Programmer Team Updated: November 3, 2025 🌐 English

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

Provider: Udemy (Listed via CoursesWyn)

Instructor: Lazy Programmer Inc., Lazy Programmer Team

Coupon Verified On: November 3, 2025

Difficulty Level: All Levels

Category: Development

Subcategory: Reinforcement Learning

Duration: 15h 30m of on-demand video

Language: English

Access: Lifetime access to all course lectures and updates

Certificate: Official certificate of completion issued by Udemy upon finishing all course requirements

Top Learning Outcomes: 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)

Prerequisites: 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.

Price: $10.99 with coupon / Regular Udemy price: $109.99. Applying this coupon saves you $99.00 (90% OFF).

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What You'll Learn

The following technical skills represent the core curriculum targets for learners enrolling in this verified program today.

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

Please review the following prerequisites to ensure you have the necessary tools and foundational knowledge for this training.

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.

About This Course

Comprehensive curriculum analysis and educational value proposition from the official provider library hubs.

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!

Meet Your Instructor

Academic background and professional track record of the subject matter expert responsible for this curriculum.

L

Lazy Programmer Inc., Lazy Programmer Team

Verified Architect

A global leader with specialized excellence in Development. Instructors are vetted for curriculum quality, responsiveness, and consistent student success across the Udemy platform.

4.8 / 5.0
Instructor Rating
94% +
Success Rate

Course Comparison

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Feature Benchmarks This Verified Offer Global Standard
Cost Verification FREE (100% Validated) Fixed Subscription Fee
Enrollment Type Professional Lifetime Access Limited Time Ownership
Certification Award Included with Access Code Required Add-on Fee

Expert Review

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Andrew Derek
Lead Course Analyst, CoursesWyn

"After auditing the curriculum depth and verifying the live access protocol, Advanced AI: Deep Reinforcement Learning in PyTorch (v2) stands as an essential career asset. For a verified cost of $0, the return-on-learning ratio far exceeds commercial alternatives."

Strategic Advantages

  • Official Certificate: Credential generated at no cost.

  • Mobile Friendly: Full access via smart TV & mobile.

  • Expert Pacing: Modular design for professional schedules.

Considerations

  • Technical Depth: Requires focused 10+ hours study.

  • Tool Prep: Certain labs require proprietary software setups.

Verification Outcome: Exceptional Academic Value

Course Rating

Collective learner data and performance analytics based on verified alumni feedback loops and technical graduation audits.

4.7
★★★★★
Verified Excellence
5 Stars
88%
4 Stars
7%
3 Stars
3%
2 Stars
1%
1 Stars
1%

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Andrew Derek

Andrew Derek

Expert Reviewer

Andrew Derek is a lead editor and course analyst at CoursesWyn with over 8 years of experience in online education and digital marketing. He meticulously audits every Udemy coupon and course syllabus to ensure students get the highest quality learning materials at the best possible price.

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