Model Predictive Control

Markov Decision Process (MDP)

Contractions

Asynchronous value iteration

Linear–quadratic regulator (LQR)

Differential dynamic programming (DDP)

Quadruped locomotion

Partially observable Markov decision process (POMDP)

Bandits

Separation Principle

Dynamics Modeling

Kalman Filtering

Policy Gradient

Temporal difference (TD) learning

State–action–reward–state–action (SARSA)

TD-Gammon

Reward Shaping

Linear Programming Approach

Inverse reinforcement learning

Simultaneous Localization and Mapping (SLAM)

Linearly-solvable Markov decision problems

Learning to walk

Q-learning

Function approximation

Exploration / Exploitation

Dynamic programming

Policy Iteration

CS 294-40: Learning for robotics and control UC Berkeley Fall 2008 This advanced course focuses on the applications of machine learning in the robotics and control field. It covers a wide range of topics including Markov Decision Processes, control theories, estimation methodologies, and robotics principles. Recommended for graduate students. This is an advanced course in learning for robotics and control. The goal of this course is to help the audience with their research in learning for robotics and control or related topics. A tentative list of topics includes:

- Markov decision processes: value iteration, policy iteration, linear programming, Q learning, TD, value function approximation, inverse reinforcement learning
- Control: linear quadratic regulator, differential dynamic programming, receding horizon / model predictive control
- Estimation: (extended) Kalman filters, particle filters, SLAM
- Robotics: basic principles of various robots, sensors, microcontrollers
- Exploration/Exploitation: bandits, no-regret, e^3  Familiarity with mathematical proofs, machine learning, artificial intelligence, optimization, probability, algorithms, linear algebra; ability to implement algorithmic ideas in code (C/C++ and matlab).

Graduate students only (consent of instructor required for undergraduate students, please talk to me after first lecture and hand me summary of relevant classes/experience so I can decide whether to make an exception). 

CS 294-40: Learning for robotics and control

Robotics

Model predictive control

Model Predictive Control

1 courses cover this concept

CS 294-40: Learning for robotics and control