Reinforcement Learning Stanford

Description

Reinforcement Learning: Stanford University’s Graduate Curriculum

This course captures the depth and rigor of Stanford’s graduate-level reinforcement learning class—taught by leading researchers in the field. You’ll go beyond tutorials to master the **mathematical foundations**, **algorithmic design**, and **practical implementation** of modern RL systems used in robotics, finance, and AI research.

What You’ll Master

• Dynamic programming—value iteration, policy iteration, modified policy iteration
• Monte Carlo & Temporal Difference methods—on-policy vs off-policy, TD(λ)
• Function approximation—linear methods, neural networks, convergence guarantees
• Policy gradient theorems—REINFORCE, natural gradients, TRPO intuition
• Exploration theory—UCB, Thompson sampling, information-directed sampling

Projects & Assignments

• Solve Gridworld with dynamic programming
• Implement TD Control on the Racetrack environment
• Build a linear value approximator for MountainCar
• Derive and code the policy gradient theorem from first principles

Why Stanford’s Approach?

• Rigorous, not rushed—you’ll understand *why* algorithms work, not just how to call them
• Balances theory and code—proofs paired with Python implementations
• Prepares you for research—covers topics from Sutton & Barto and beyond

Who Should Take This?

• Graduate students in CS, AI, or robotics
• ML engineers aiming for RL specialist roles
• Researchers needing a structured, university-grade reference
• Serious practitioners tired of superficial tutorials

From Intuition to Proof to Code

This course doesn’t just teach reinforcement learning—it teaches you to **think like an RL scientist**.

Ready to master RL the Stanford way? Enroll now.