MS in Reinforcement Learning & Autonomous Systems

Autonomous systems and robotics

Program Overview

The MS in Reinforcement Learning & Autonomous Systems trains the next generation of researchers and engineers working on sequential decision-making under uncertainty. The program covers the mathematical foundations of RL, state-of-the-art algorithms, and their applications in robotics, game AI, autonomous driving, and industrial control.

Curriculum Highlights

  • Foundations: Markov decision processes, dynamic programming, Monte Carlo methods, temporal difference learning
  • Policy Optimization: Policy gradients (REINFORCE, PPO, SAC), actor-critic methods, trust region methods
  • Model-Based RL: World models, Dyna, MBPO; learning environment models; sim-to-real transfer
  • Deep RL: DQN, Rainbow; deep policy gradients; multi-agent RL; offline RL (IQL, CQL, TD3+BC)
  • Applications: Robotic manipulation, locomotion, autonomous driving, game AI, industrial optimization

Sample Courses

  • RL-401: Markov Decision Processes and Dynamic Programming
  • RL-410: Policy Gradient Methods and Actor-Critic Algorithms
  • RL-420: Deep Reinforcement Learning
  • RL-430: Model-Based RL and World Models
  • RL-440: Multi-Agent RL and Game Theory
  • RL-450: Sim-to-Real Transfer and Physical Systems
  • RL-490: Capstone: Autonomous System Project