Multi-agent Ludo Game Collaborative Path Planning based on Markov Decision Process

2021 ◽  
pp. 37-51
Author(s):  
Mohammed El Habib Souidi ◽  
Toufik Messaoud Maarouk ◽  
Abdeldjalil Ledmi
Keyword(s):  
Path Planning ◽  
Markov Decision Process ◽  
Decision Process ◽  
Markov Decision ◽  
Multi Agent

scholarly journals COG-DICE: An Algorithm for Solving Continuous-Observation Dec-POMDPs

2017 ◽  
Author(s):  
Madison Clark-Turner ◽  
Christopher Amato
Keyword(s):  
Markov Decision Process ◽  
Real World ◽  
Decision Process ◽  
Extended Version ◽  
Continuous Observation ◽  
Solution Methods ◽  
Markov Decision ◽  
Multi Agent ◽  
Partially Observable Markov ◽  
Partially Observable

The decentralized partially observable Markov decision process (Dec-POMDP) is a powerful model for representing multi-agent problems with decentralized behavior. Unfortunately, current Dec-POMDP solution methods cannot solve problems with continuous observations, which are common in many real-world domains. To that end, we present a framework for representing and generating Dec-POMDP policies that explicitly include continuous observations. We apply our algorithm to a novel tagging problem and an extended version of a common benchmark, where it generates policies that meet or exceed the values of equivalent discretized domains without the need for finding an adequate discretization.

scholarly journals The Convergence of a Cooperation Markov Decision Process System

Entropy ◽  
2020 ◽  
Vol 22 (9) ◽  
pp. 955
Author(s):  
Xiaoling Mo ◽  
Daoyun Xu ◽  
Zufeng Fu
Keyword(s):  
Markov Decision Process ◽  
Decision Process ◽  
Optimal Strategy ◽  
Value Function ◽  
Single Agent ◽  
Two Agents ◽  
Markov Decision ◽  
Process System ◽  
Multi Agent ◽  
Game Environment

In a general Markov decision progress system, only one agent’s learning evolution is considered. However, considering the learning evolution of a single agent in many problems has some limitations, more and more applications involve multi-agent. There are two types of cooperation, game environment among multi-agent. Therefore, this paper introduces a Cooperation Markov Decision Process (CMDP) system with two agents, which is suitable for the learning evolution of cooperative decision between two agents. It is further found that the value function in the CMDP system also converges in the end, and the convergence value is independent of the choice of the value of the initial value function. This paper presents an algorithm for finding the optimal strategy pair (πk0,πk1) in the CMDP system, whose fundamental task is to find an optimal strategy pair and form an evolutionary system CMDP(πk0,πk1). Finally, an example is given to support the theoretical results.

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