Reinforcement learning for procurement agents of factory of the future

Simsek, Burak; Albayrak, Şahin; Korth, A.

doi:10.1109/cec.2004.1331051

Cited by 3 publications

(2 citation statements)

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“…P s , j (a t ) is the transition probability from state s t to state j. For further information about our RL algorithm and the choice of the parameters please see [13].…”

Section: Reinforcement Learningmentioning

confidence: 99%

“…In fact the number of runs during the training period affects the accuracy of the decisions after training in case the training data does not fluctuate very much [13]. However if there are fluctuations, it makes sense to use less training data to capture the effects of such fluctuations as it will be mentioned in section 5.…”

Section: Simulation With Reinforcement Learningmentioning

confidence: 99%

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Dynamic Decision Making for Candidate Access Point Selection

Simsek

Wolter

Coşkun

2006

Autonomic Networking

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In this paper, we solve the problem of candidate access point selection in 802.11 networks, when there is more than one access point available to a station. We use the QBSS (quality of service enabled basic service set) Load Element of the new WLAN standard 802.11e as prior information and deploy a decision making algorithm based on reinforcement learning. We show that using reinforcement learning, wireless devices can reach more efficient decisions compared to static methods of decision making which opens the way to a more autonomic communication environment. We also present how the reinforcement learning algorithm reacts to changing situations enabling self adaptation. The authors Burak Simsek and Katinka Wolter would like to thank German Research Foundation (DFG) for support of this work under grant number WO 898/1-2.

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“…P s , j (a t ) is the transition probability from state s t to state j. For further information about our RL algorithm and the choice of the parameters please see [13].…”

Section: Reinforcement Learningmentioning

confidence: 99%

Section: Simulation With Reinforcement Learningmentioning

confidence: 99%

Dynamic Decision Making for Candidate Access Point Selection

Simsek

Wolter

Coşkun

2006

Autonomic Networking

View full text Add to dashboard Cite

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Policy Transition of Reinforcement Learning for an Agent Based SCM System

Zhao

Sun

2006

2006 IEEE International Conference on Industrial Informatics

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Reinforcement Learning (RL) is successfully applied to some dynamical and unpredictable domains. The Supply Chain Management (SCM) is NP-hard problem. Some proposed RL methods perform better than traditional tools for dynamic problem solving in SCM. It realizes on-line learning and performs efficiently in some applications, but RL agent reacts worse than some heuristic methods to sudden changes in SCM demand since the trial-and-error characteristic of RL is time-consuming in practice. By surveying an efficient policy transition mechanism in RL about how to mapping existing policies in the previous task to a new policies in a changed task, this paper proposes a novel RL agent based SCM system that decreases learning time of the RL agent to a dynamic environment. As the result, the RL agent derives the maximal profit using RL technique as jobs coming with a stable distribution. Further, the RL agent makes the optimal procurement satisfying the requirement of sudden changes in the supply chain network by the policy transition mechanism.

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