Learning of Cooperative actions in multi-agent systems: a case study of pass play in Soccer
نویسندگان
چکیده
From the standpoint of multi-agent systems, soccer (association football), which is just one of usual team sports, make a good example of problems in the real world which is moderately abstracted. We have chosen soccer as one of standard problems for the study on multi-agent systems, and we are now developing a soccer server, which provides a common test-bench to various multi-agent systems. We present an experiment on cooperative action learning among soccerplayer agents on the server. Our soccer agent can learn whether he should shoot a ball or pass it.
منابع مشابه
Learning Cooperative Behavior in Multi-Agent Environment - A Case Study of Choice of Play-Plans in Soccer
Soccer, association football, is a typical team-game, and is considered as a standard problem of multi-agent system and cooperative computation. We are developing Soccer Server, a simulator of soccer, which provides a common test-bench to evaluate various multi-agent systems and cooperative algorithms. We are working on learning cooperative behavior in multi-agent environment using the server. ...
متن کاملUtilizing Generalized Learning Automata for Finding Optimal Policies in MMDPs
Multi agent Markov decision processes (MMDPs), as the generalization of Markov decision processes to the multi agent case, have long been used for modeling multi agent system and are used as a suitable framework for Multi agent Reinforcement Learning. In this paper, a generalized learning automata based algorithm for finding optimal policies in MMDP is proposed. In the proposed algorithm, MMDP ...
متن کاملAn Unsupervised Learning Method for an Attacker Agent in Robot Soccer Competitions Based on the Kohonen Neural Network
RoboCup competition as a great test-bed, has turned to a worldwide popular domains in recent years. The main object of such competitions is to deal with complex behavior of systems whichconsist of multiple autonomous agents. The rich experience of human soccer player can be used as a valuable reference for a robot soccer player. However, because of the differences between real and simulated soc...
متن کاملImproving Agent Performance for Multi-Resource Negotiation Using Learning Automata and Case-Based Reasoning
In electronic commerce markets, agents often should acquire multiple resources to fulfil a high-level task. In order to attain such resources they need to compete with each other. In multi-agent environments, in which competition is involved, negotiation would be an interaction between agents in order to reach an agreement on resource allocation and to be coordinated with each other. In recent ...
متن کاملPrioritizing Pass Options by Reinforcement of Simulated Soccer Agents
Learning to act and cooperate in dynamic multi-agent environments can be an excessively complex task, especially when it comes to imitating natural biological multi-agent systems (MAS). RoboCup simulated soccer is a multi-agent environment which presents many challenges to cooperative learning algorithms, including a large state space, hidden and uncertain states, multiple heterogeneous indepen...
متن کامل