基于决策市场的多智能体上下文Bandit问题学习
Decision Market Based Learning For Multi-agent Contextual Bandit Problems
摘要：
信息通常以分布式和专有的形式存储，拥有信息的代理人通常是自私的，需要激励才能透露他们的信息。需要适当的机制来引出和聚集这种分布式信息以用于决策。在本文中，我们使用模拟来研究使用决策市场的机制，在多智能体学习系统，以聚合分布式信息的决策在上下文强盗问题。该系统利用严格适当的决策评分规则来评估代理的概率报告的准确性，这使得代理能够学习联合解决上下文强盗问题。我们的模拟表明，我们的多智能体系统与分布式信息可以有效地训练作为一个集中的对应与一个单一的代理，接收所有信息。此外，我们使用我们的系统来调查的情况下，确定性的决策评分规则是不激励兼容。我们观察到出现了更复杂的动力学与操纵行为，这与现有的理论分析。
英文摘要：
Information is often stored in a distributed and proprietary form, and agents who own information are often self-interested and require incentives to reveal their information. Suitable mechanisms are required to elicit and aggregate such distributed information for decision making. In this paper, we use simulations to investigate the use of decision markets as mechanisms in a multi-agent learning system to aggregate distributed information for decision-making in a contextual bandit problem. The system utilises strictly proper decision scoring rules to assess the accuracy of probabilistic reports from agents, which allows agents to learn to solve the contextual bandit problem jointly. Our simulations show that our multi-agent system with distributed information can be trained as efficiently as a centralised counterpart with a single agent that receives all information. Moreover, we use our system to investigate scenarios with deterministic decision scoring rules which are not incentive compatible. We observe the emergence of more complex dynamics with manipulative behaviour, which agrees with existing theoretical analyses.
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。
