---
title: "OMSCS CS7642 (Reinforcement Learning) Review and Tips"
url: https://daily.dev/posts/omscs-cs7642-reinforcement-learning-review-and-tips-e3ioevmza
source_url: https://eugeneyan.com/writing/omscs-cs7642-reinforcement-learning
type: article
source: "Eugene Yan"
published: 2026-05-31T07:40:05.888Z
updated: 2026-05-31T08:06:27.387Z
tags: ["python", "reinforcement-learning"]
reading_time: 7
upvotes: 0
comments: 0
language: en
---

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# OMSCS CS7642 (Reinforcement Learning) Review and Tips

**[Eugene Yan](https://daily.dev/sources/eugeneyan)** · 7 min read · 0 upvotes · 0 comments

## Summary

A review of Georgia Tech's OMSCS CS7642 Reinforcement Learning course, covering course structure, workload, and key projects. The course spans model-based methods (value/policy iteration), model-free methods (Q-learning, deep Q-learning), and multi-agent reinforcement learning. A highlight project involved training a deep RL agent to land a rocket in OpenAI's LunarLander environment using Double DQN. The course is workload-heavy (30–40 hrs/week), includes three paper-replication projects, weekly homework, and a final exam. Tips include leveraging David Silver's videos and TA office hours.

## Full article

daily.dev links to this article rather than hosting it. Read it at the original source: <https://eugeneyan.com/writing/omscs-cs7642-reinforcement-learning>

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Tags: [#python](https://daily.dev/tags/python), [#reinforcement-learning](https://daily.dev/tags/reinforcement-learning)

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