Hands-on Reinforcement Learning with PyTorch tutorial

Description

Hands-On Reinforcement Learning with PyTorch: From Theory to Intelligent Agents

Reinforcement Learning (RL) powers the most advanced AI systems—from AlphaGo to self-driving cars. This course cuts through the math-heavy tutorials and gives you a practical, code-first approach to building real RL agents using PyTorch. You’ll implement DQN, A2C, PPO, and more—on environments like CartPole, Atari, and custom simulators.

What You’ll Build

• A Deep Q-Network (DQN) that masters CartPole and Lunar Lander
• An Advantage Actor-Critic (A2C) agent that learns continuous control
• A Proximal Policy Optimization (PPO) system for stable, sample-efficient learning
• A custom RL environment for your own problem domain

Why PyTorch for RL?

• Dynamic computation graphs make debugging intuitive
• Eager execution by default—write Python, not symbolic graphs
• Seamless GPU acceleration for faster training
• Industry adoption—used by FAIR, DeepMind (for research), and startups

Skills You’ll Master

• Markov Decision Processes (MDPs) and Bellman equations
• Q-learning, policy gradients, and actor-critic methods
• Experience replay, target networks, and entropy regularization
• Environment design with OpenAI Gym and custom wrappers
• Logging, visualization, and hyperparameter tuning

Who Is This For?

• Intermediate Python developers exploring AI frontiers
• Deep learning practitioners expanding into sequential decision-making
• Students building capstone projects in autonomous systems
• Researchers needing a practical PyTorch RL reference

From Zero to Research-Ready

Each project includes starter code, well-commented implementations, and performance baselines—so you focus on learning, not debugging obscure errors.

Ready to build agents that learn by doing? Enroll now.