Practical Reinforcement Learning for ML Engineers

About this course

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 learn RL in practice - Undergraduate and postgraduate students in AI/ML - Developers with basic ML knowledge who want to understand RL from scratch ## Requirements- Basic Pyth

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