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arXiv 1402.4525cs.AI
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RoboCup三维足球仿真中动态角色划分的非策略通用价值函数

Off-Policy General Value Functions to Represent Dynamic Role Assignments in RoboCup 3D Soccer Simulation

University of Miami(迈阿密大学)

Saminda Abeyruwan, Andreas Seekircher, Ubbo Visser

摘要:

在动态、复杂、对抗性和随机的环境中收集和维护准确的世界知识,如RoboCup 3D足球仿真是一项具有挑战性的任务。知识应该在时间限制下实时学习。我们在强化学习中使用最近引入的非策略梯度下降算法,该算法说明了动态角色分配的可学习知识表示。研究结果表明,在2012年RoboCup的三对三、五对五和七对七的比赛中,智能体已经学会了与顶级球队的竞争策略。我们已经明确地使用代理的子集来识别的动态和语义的代理学习,以最大限度地提高他们的性能指标,并收集有关不同目标的知识,使所有代理有效地参与组内。

英文摘要:

Collecting and maintaining accurate world knowledge in a dynamic, complex, adversarial, and stochastic environment such as the RoboCup 3D Soccer Simulation is a challenging task. Knowledge should be learned in real-time with time constraints. We use recently introduced Off-Policy Gradient Descent algorithms within Reinforcement Learning that illustrate learnable knowledge representations for dynamic role assignments. The results show that the agents have learned competitive policies against the top teams from the RoboCup 2012 competitions for three vs three, five vs five, and seven vs seven agents. We have explicitly used subsets of agents to identify the dynamics and the semantics for which the agents learn to maximize their performance measures, and to gather knowledge about different objectives, so that all agents participate effectively and efficiently within the group.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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