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Multiagent systems are one of the most promising solutions in most of real life applications in which some kinds of social interactions or conventions are involved. Agent oriented applications are broadly explored among which learning in unknown environment is well developed based on Markov Decision Process (MDP). On the other hand, learning in multiagent systems has been recently introduced, basically...
Recent developments in multiagent reinforcement learning, mostly concentrate on normal form games or restrictive hierarchical form games. In this paper, we use the well known Q-learning in extensive form games which agents have a fixed priority in action selection. We also introduce a new concept called associative Q-values which not only can be used in action selection, leading to a subgame perfect...
This paper investigates a new algorithm in Multi-agent Reinforcement Learning. We propose a multi-agent learning algorithm that is extend single agent actor-critic methods to the multi-agent setting. To realize the algorithm, we introduced the value of agentpsilas temporal best-response strategy instead of the value of an equilibria. So, our algorithm uses the linear programming to compute Q values...
Multiagent systems are rapidly finding applications in a variety of domains, including robotics, distributed control, telecommunications, and economics. The complexity of many tasks arising in these domains makes them difficult to solve with preprogrammed agent behaviors. The agents must, instead, discover a solution on their own, using learning. A significant part of the research on multiagent learning...
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