Neuro-Dynamic Programming
On-Line Support Vector Machine Regression
ECML '02 Proceedings of the 13th European Conference on Machine Learning
Value Function Based Production Scheduling
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Multi-agent coordination and control using stigmergy
Computers in Industry
A reinforcement learning approach to job-shop scheduling
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
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The paper investigates a stochastic production scheduling problem with unrelated parallel machines. A closed-loop scheduling technique is presented that on-line controls the production process. To achieve this, the scheduling problem is reformulated as a special Markov Decision Process. A near-optimal control policy of the resulted MDP is calculated in a homogeneous multi-agent system. Each agent applies a trial-based approximate dynamic programming method. Different cooperation techniques to distribute the value function computation among the agents are described. Finally, some benchmark experimental results are shown.