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Multi-Robot Systems
I taught a 30-week course about Multi-Robot Systems (MRS) at HKUST. This course is a combination of some Robotics, SLAM, Multi-Agent Systems, Reinforcement Learning, and Game Theory topics, which are related to MRS.
References
References
Course Slides
Course Slides
If you require the complete course material or encounter any difficulties, please download the zip file.
Lecture1_Introduction to Multi-Robot Systems I (HKUST)
Lecture2_Introduction to Multi-Robot Systems II (HKUST)
Lecture3_Motion Control (HKUST)
Lecture4_Sensors and Perception (link from ETH)
Lecture5_Localization (link from CMU)
Lecture6_Navigation and Path Planning (link from CMU)
Lecture7_Intro to Reinforcement Learning (link from UCL)
Lecture8_Multiarmed Bandits (link from MIT)
Lecture9_Markov Decision Processes (link from UC Berkeley)
Lecture10_Dynamic Programming (link from UCL)
Lecture11_1_Monte Carlo Methods (link from UMass)
Lecture11_2_Importance Sampling (slides from UC Irvine)
Lecture12_Temporal Difference Learning (link from NEU)
Lecture13_Planning I (link from CMU)
Lecture14_Planning II (link from CMU)
Lecture15_Value Function Approximation (link from David Silver)
Lecture16_Policy Gradient Methods (link from David Silver)
Lecture17_Deep Learning (link from MIT)
Lecture18_Deep Reinforcement Learning (link from CUHK)
Lecture19_Multi-Robot Path Planning (link from CMU)
Lecture20_Reading: Multi-Agent Reinforcement Learning I (link from TU Delft)
Lecture21_Reading: Multi-Agent Reinforcement Learning II (paper from Oxford)
Lecture22_Intro to Game Theory (link from CMU)
Lecture23_Bayesian Games (link from Harvard)
Lecture24_Learning in Repeated Games (link from Jun Wang)
Lecture25_Reading: Multi-Agent Reinforcement Learning III (paper from Oxford)
Lecture26_Reading: Multi-Agent Reinforcement Learning IV (paper from UCL)
Lecture27_Intro to Mechanism Design (link from USTC)
Lecture28_Reading: Multi-Agent Reinforcement Learning V (paper from Oxford)
Lecture29_Reading: Multi-Agent Reinforcement Learning VI
Lecture30_Reading: A Survey of Multi-Objective Sequential Decision-Making
WE ARE NOT DONE YET!
Boost List:
Decision Making Under Uncertainty: Theory and Application
Reinforcement Learning: Theory and Algorithms
Carlos Ernesto Guestrin's Ph.D. Thesis
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