A hybrid approach for dynamic task scheduling in unforeseen environments using multi agent reinforcement learning and enhanced Q-learning
Shayalika, Jayakody Kankanamlage Chathurangi
A hybrid approach for dynamic task scheduling in unforeseen environments using multi agent reinforcement learning and enhanced Q-learning - [2020] - xv, 86p. : col. ill. CD-ROM
MULTI AGENT REINFORCEMENT LEARNING
DEEP Q-LEARNING
Q-LEARNING.
DYNAMIC TASK SCHEDULING
UNFORESEEN ENVIRONMENT
INFORMATION TECHNOLOGY -Dissertation
COMPUTATIONAL MATHEMATICS -Dissertation
ARTIFICIAL INTELLIGENCE -Dissertation
MSc in Artificial Intelligence
004.8(043)
A hybrid approach for dynamic task scheduling in unforeseen environments using multi agent reinforcement learning and enhanced Q-learning - [2020] - xv, 86p. : col. ill. CD-ROM
MULTI AGENT REINFORCEMENT LEARNING
DEEP Q-LEARNING
Q-LEARNING.
DYNAMIC TASK SCHEDULING
UNFORESEEN ENVIRONMENT
INFORMATION TECHNOLOGY -Dissertation
COMPUTATIONAL MATHEMATICS -Dissertation
ARTIFICIAL INTELLIGENCE -Dissertation
MSc in Artificial Intelligence
004.8(043)