In this paper, multiagent reinforcement learning has been used in wargame simulation. Military organization causes priorities in actions taken among agents in the feild. Decision making among n-person generalsum multiagent systems with consecutive action selection can be modeled through extensive form games. The existing process of learning can be considered as a set of successive extensive form games with Markov property. We use subgame perfect equilibrium points as optimal joint action using the well-known backward induction algorithm. An estimation of possible utilities gained over a game with respect to other agents’ preferences, called as associative Q-values, is used to predict optimal action. Simulation results present the effectiveness of the proposed algorithms in wargame simulation.
Akrami Zadeh, A., Afshar, A., Menhaj, M. B., & Jafari, S. (2010). Military Units Optimal Formation in Wargame Simulation Using Multiagent Reinforcment Learning. Electronics Industries, 1(1), 77-101.
MLA
Ali Akrami Zadeh; Ahmad Afshar; Mohammad Bagher Menhaj; Samira Jafari. "Military Units Optimal Formation in Wargame Simulation Using Multiagent Reinforcment Learning". Electronics Industries, 1, 1, 2010, 77-101.
HARVARD
Akrami Zadeh, A., Afshar, A., Menhaj, M. B., Jafari, S. (2010). 'Military Units Optimal Formation in Wargame Simulation Using Multiagent Reinforcment Learning', Electronics Industries, 1(1), pp. 77-101.
VANCOUVER
Akrami Zadeh, A., Afshar, A., Menhaj, M. B., Jafari, S. Military Units Optimal Formation in Wargame Simulation Using Multiagent Reinforcment Learning. Electronics Industries, 2010; 1(1): 77-101.
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