Dynamic Policy Adaptation for Collaborative Groups

Author(s):  
Madhumita Chatterjee ◽  
G. Sivakumar
Author(s):  
Kevin J. S. Zollman

This article presents a rudimentary model of collaboration with the aim to understand the conditions under which groups of scientists will endogenously form optimal collaborative groups. By analyzing the model with computer simulations, I uncover three lessons for collaborative groups. First, in reducing the cost borne by scientists from collaborating, one benefits the members of the group. Second, increasing the number of potential collaborative partners benefits all those involved in a collaborative group. Finally and counter intuitively, this model suggests that groups do better when scientists avoid experimenting with new collaborative interactions.


2020 ◽  
pp. 1-15
Author(s):  
Tristan Cazenave ◽  
Jean-Yves Lucas ◽  
Thomas Triboulet ◽  
Hyoseok Kim

Nested Rollout Policy Adaptation (NRPA) is a Monte Carlo search algorithm that learns a playout policy in order to solve a single player game. In this paper we apply NRPA to the vehicle routing problem. This problem is important for large companies that have to manage a fleet of vehicles on a daily basis. Real problems are often too large to be solved exactly. The algorithm is applied to standard problem of the literature and to the specific problems of EDF (Electricité De France, the main French electric utility company). These specific problems have peculiar constraints. NRPA gives better result than the algorithm previously used by EDF.


Author(s):  
Germán Gieczewski ◽  
Christopher Li
Keyword(s):  

Author(s):  
Ronny Bianchi ◽  
Aldo Enrietti ◽  
Renato Lanzetti
Keyword(s):  

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