Reinforcement Learning for Robotics (Lecture with Project)
The course will cover the following topics:
1. Introduction to reinforcement learning (RL): Markov decision process, dynamic programming, Q-learning, SARSA, Actor-Critic, policy-based RL, value-based RL.
2. Reinforcement learning in continuous state-action spaces. Function approximation problem.
3. Reinforcement learning for robotics: mission and problems. Optimal control. Biased sampling, risk of damage, ware-out problem.
4. Model-free reinforcement learning (GMMRL, PI2).
5. Model-based reinforcement learning (PILCO, PI-REM).
6. Approaches combining nonlinear optimal control (ILQR, MPC) and reinforcement learning.
7. Introduction to deep reinforcement learning (end-to-end approaches).
Previous Knowledge Expected
Fundamentals of Linear Algebra, Probability and Statistics, Programming skills in Matlab/SImulink
At the end of this course, students are able to:
- Implement machine learning algorithms for robots and autonomous systems.
- Evaluate the performance of reinforcement learning algorithms.