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Realtime Search for Learning Autonomous Agents

实时搜索学习自主代理

基本信息

批准号：
06452402
负责人：
ISHIDA Toru
金额：
$ 4.86万
依托单位：
KYOTO UNIVERSITY
依托单位国家：
日本
项目类别：
Grant-in-Aid for Scientific Research (B)
财政年份：
1994
资助国家：
日本
起止时间：
1994 至 1996
项目状态：
已结题

来源：
https://kaken.nii.ac.jp/grant/KAKENHI-PROJECT-06452402/
关键词：
Realtime search learning agent problem solving 強化学習 TD(λ)Q-learning

项目摘要

Existing search algorithms can be divided into two classes : offline search such as A^<**>, and realtime search such as Real-Time-A^<**> (RTA^<**>) and Learning Real-Time-A^<**> (LRTA^<**>). Offline search completely examines every possible path to the goal state before executing that path, while realtime search makes each decision in a constant time, and commits its decision to the physical world. The problem solver eventually reaches the goal by repeating the cycle of planning and execution. Realtime search cannot guarantee to find an optimal solution, but can interleave planning and execution.This research focuses on extending realtime search algorithms for autonomous agents and for a multiagent world. Though realtime search provides an attractive framework for resource-bounded problem solving, the behavior of the problem solver is not rational enough for autonomous agents : the problem solver tends to perform superfluous actions before attaining the goal ; and the problem solver cannot utilize and improve previous experiments. Other problems are that though the algorithms interleave planning and execution, they cannot be directly applied to a multiagent world ; the problem solver cannot adapt to the dynamically changing goals ; and the problem solver cannot cooperatively solve problems with other problem solvers. We developed a series of new algorithms including Weighted Realtime Search, Realtime Search with Upper Bounds, Moving Target Search and Realtime Bidirectional Search to overcome the above problems.

现有的搜索算法可以分为两类：离线搜索，例如A1 <**>，以及实时搜索，例如实时-A1 <**>（RTA <**>）和学习实时-A1 <**>（LRTA <**>）。离线搜索在执行目标状态之前会完全检查每一条可能的路径，而实时搜索在恒定的时间内做出每个决策，并将其决策提交给物理世界。问题解决者通过重复计划和执行的循环最终达到目标。实时搜索不能保证找到一个最优解，但可以交错规划和execution.This研究的重点是扩展实时搜索算法的自治代理和多智能体的世界。尽管实时搜索为资源有限的问题解决提供了一个有吸引力的框架，但问题解决者的行为对于自治代理来说不够合理：问题解决者往往在实现目标之前执行多余的动作;并且问题解决者无法利用和改进之前的实验。其他的问题是，虽然算法交错规划和执行，他们不能直接应用到多智能体世界;问题解决者不能适应动态变化的目标;和问题解决者不能合作解决问题与其他问题解决者。为了克服上述问题，我们提出了一系列新的算法，包括加权实时搜索、上界实时搜索、移动目标搜索和实时双向搜索。