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DOI：
10.1109/icmas.2000.858517
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发表时间：
2000-07
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我们提出了一种开发多智能体强化学习系统的方法，该系统由模块化智能体联盟组成。我们专注于学习分割动作序列以在强化学习中创建模块化结构，通过添加基于任务执行期间收到的强化的额外投标过程。该方法的片段序列和代理之间的分布，以促进学习的整体任务。值得注意的是，我们的方法不依赖于先验知识或先验结构。初步实验证明了该方法的基本承诺。这项工作展示了如何将竞价和强化学习有效地结合起来，从而指出了一种新的且有前途的方法。
We present an approach for developing multi-agent reinforcement learning systems that are made up of a coalition of modular agents. We focus on learning to segment action sequences to create modular structures in reinforcement learning, through adding an additional bidding process that is based on reinforcements received during task execution. The approach segments sequences and distributes them among agents to facilitate the learning of the overall task. Notably, our approach does not rely on a priori knowledge or a priori structures. Initial experiments demonstrated the basic promise of the approach. This work shows how bidding and reinforcement learning can be usefully combined, thus pointing to a new and promising approach.