Modern Reinforcement Learning: Deep Q Agents (PyTorch & TF2)

How to Turn Deep Reinforcement Learning Research Papers Into Agents That Beat Classic Atari Games

4.34 (1092 reviews)

Udemy

platform

English

language

Data Science

What you will learn

How to read and implement deep reinforcement learning papers

How to code Deep Q learning agents

How to Code Double Deep Q Learning Agents

How to Code Dueling Deep Q and Dueling Double Deep Q Learning Agents

How to write modular and extensible deep reinforcement learning software

How to automate hyperparameter tuning with command line arguments

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Our Verdict

Modern Reinforcement Learning: Deep Q Agents (PyTorch & TF2) offers a meticulous and professional approach to diving into complex deep reinforcement learning. While providing students with pro-level materials, the course may fall short in offering adequate assistance in dealing with various issues arising from different environments. Although this Udemy course might not deliver the greatest explanatory step-by-step code, it compensates for that by encouraging users to consult other resources and learn from them organically, fostering curiosity and self-sufficiency in students.

What We Liked

Excellent for understanding the practical implementation of deep reinforcement learning research papers
Invaluable skills imparted in breaking down and implementing algorithms from peer-reviewed articles
Comprehensive, professional course material aimed at aspiring professionals
Focus on implementing DQN, Double DQN, Dueling DDQN as well as Deep Q Learning agents

Potential Drawbacks

Significant gaps between the theory explained and the complexity of assignments
Code often doesn't work as implemented in the course, requiring external resources to fix dependency issues
Increasing complexity can lead to a decrease in teaching quality, with some vital theory left pending
Lacks thorough explanation of certain equations used throughout research papers