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Module DEEP REINFORCEMENT LEARNING FOR ROBOTS

Module code: CS637
Credits: 5
Semester: 2
Department: COMPUTER SCIENCE
International: No
Overview Overview
 

Aims:

Introduction to reinforcement learning (RL): Markov decision process, dynamic programming, Q-learning, SARSA, Actor-Critic, policy-based RL, value-based RL.
Reinforcement learning in continuous state-action spaces. Function approximation problem.
Reinforcement learning for robotics: mission and problems. Optimal control. Biased sampling, risk of damage, ware-out problem.
Model-free reinforcement learning (GMMRL, PI2).
Model-based reinforcement learning (PILCO, PI-REM).
Approaches combining nonlinear optimal control (ILQR, MPC) and reinforcement learning.
Introduction to deep reinforcement learning (end-to-end approaches).

Open Learning Outcomes
 
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