Prediction and Planning: Bridging the Gap

这个项目是为了提高智能软件代理的性能而进行的基础研究，它基于这样的观察：代理过去的经验是一个有价值的、通常没有得到充分利用的数据库。其目标是产生比现有强化学习算法更有效地利用数据的算法，使人们能够将代理存储的经验作为一个储存库来查看，从而可以挖掘出提高性能的信息。更广泛地说，代理人可以选择使用通过观察其他代理人获得的数据，甚至从挖掘网络中获得的数据。这项研究的影响可能在许多领域都能感受到。例如，可以期望软件学习代理以更接近人类的方式学习；值得注意的经验将被记住，它们对未来性能的影响不会减弱。数据将不会有抽样要求，因此可以从观察他人中学习，并可以使用存储的数据存储库来了解新的行为。这项工作可能的实际应用包括网络管理和电子商务。

This project is fundamental research to improve the performance of intelligent software agents, based on the observation that an agent's past experiences are a valuable and generally underutilized database. The goal is to produce algorithms that make stronger use of data than existing reinforcement learning algorithms, enabling a view of the agent's stored experiences as a repository that can be mined for performance-improving information. More generally, the agent may choose to use data obtained by observing other agents, or even from mining the web.The impact of this research may be felt in many areas. For example, software learning agents can be expected to learn in a much more human-like manner; noteworthy experiences will be remembered, and their influence on future performance will not attenuate. There will be no sampling requirements on the data, so it will be possible to learn from watching others and possible to use repositories of stored data to learn new behaviors. Among the likely practical applications of this work are network management and electronic commerce.