Advanced Machine Learning Specialization – Practical RL

Description

Practical Reinforcement Learning: From Q-Learning to Deep RL with PyTorch

Reinforcement Learning (RL) powers AlphaGo, self-driving cars, and robotics—but most tutorials stop at theory. This course dives into **hands-on implementation**: build DQN, A2C, and PPO agents that solve real environments from OpenAI Gym and custom simulations.

Projects You’ll Build

• A DQN agent that plays CartPole and LunarLander
• An A2C agent for continuous control tasks
• An Atari game player using CNN + RL
• A custom RL environment for your own problem domain

Key Algorithms Covered

• Q-Learning & SARSA
• Deep Q-Networks (DQN)—with experience replay and target networks
• Policy Gradients—REINFORCE, Actor-Critic, A2C
• Advanced topics—PPO, exploration strategies, Monte Carlo Tree Search

Why This Course?

• No fluff—every lecture ends with code
• Debugging focus—learn to diagnose reward instability, vanishing gradients
• From classic to cutting-edge—covers both foundational and modern RL

Who Is This For?

• ML engineers exploring autonomous systems
• Game developers building smart NPCs
• Researchers prototyping RL solutions
• Students in AI/robotics programs

From Theory to Agent

You’ll graduate with a **portfolio of working RL agents**—not just equations.

Ready to build intelligent agents that learn from experience? Enroll now.