Practical Reinforcement Learning for ML Engineers

Learn RL intuitively from scratch with hands-on implementations of REINFORCE, Actor-Critic, PPO, DQN, and RLHF (Pytorch)

What you’ll learn
✓ Understand the intuition behind reinforcement learning and how it differs from supervised learning and imitation learning
✓ Implement REINFORCE, Actor-Critic, PPO, and DQN from scratch using Pytorch
✓ Use OpenAI Gym environments to train and evaluate reinforcement learning agents
✓ Understand how modern RL algorithms are categorized (model-free, model-based, offline RL)
✓ Understand how RL is used in training LLMs (RLHF, PPO, DPO)

Requirements
● Basic Python programming
● Familiarity with PyTorch or deep learning frameworks
● Basic understanding of machine learning and neural networks

Description
Reinforcement Learning (RL) is one of the most powerful areas in machine learning — but also one of the hardest to learn. Most RL courses are either too theoretical or too shallow.

Note: This course is taught in Arabic (with English technical terminology).

## What makes this course different?

– Intuition-first approach: we start from supervised learning and build up to RL

– Hands-on implementation: all algorithms are implemented from scratch

– Practical focus: you will work with real environments using OpenAI Gym

– Covers modern topics like RLHF (used in fine-tuning LLMs)

– Includes GitHub repositories for deeper exploration and experimentation

## What you will learn

– Understand the intuition behind reinforcement learning and how it differs from supervised learning and imitation learning

– Implement REINFORCE, Actor-Critic, PPO, and DQN from scratch using PyTorch

– Use OpenAI Gym to train and evaluate RL agents

– Understand key RL concepts: MDPs, value functions, policy gradients

– Learn how RL is used in fine-tune large language models (RLHF, PPO, DPO)

## Course structure

We build understanding step-by-step

1. From supervised learning to imitation learning

2. Introduction to reinforcement learning and REINFORCE

3. Actor-Critic methods

4. Proximal Policy Optimization (PPO)

5. Value-based methods (Q-learning and DQN)

6. Model-based RL and offline RL (high-level)

7. Advanced topics (stability, continuous actions, POMDPs)

8. Reinforcement Learning from Human Feedback (RLHF)

##

Who this course is for
■ Machine learning engineers who want to understand reinforcement learning in practice
■ Undergraduate and postgraduate students in AI/ML
■ Anyone interested in understanding how RL is used in modern systems like LLMs (RLHF)

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