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首页> 外文期刊>Journal of supercomputing >A hybrid cognitive/reactive intelligent agent autonomous path planning technique in a networked-distributed unstructured environment for reinforcement learning
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A hybrid cognitive/reactive intelligent agent autonomous path planning technique in a networked-distributed unstructured environment for reinforcement learning

机译：网络分布的非结构化环境中的混合认知/反应智能主体自主路径规划技术，用于强化学习

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This paper proposes a path planning technique for autonomous agent(s) located in an unstructured networked distributed environment, where each agent has limited and not complete knowledge of the environment. Each agent has only the knowledge available in the distributed memory of the computing node the agent is running on and the agents share some information learned over a distributed network. In particular, the environment is divided into several sectors with each sector located on a single separate distributed computing node. We consider hybrid reactive-cognitive agent(s) where we use autonomous agent motion planning that is based on the use of a potential field model accompanied by a reinforcement learning as well as boundary detection algorithms. Potential fields are used for fast convergence toward a path in a distributed environment while reenforcement learning is used to guarantee a variety of behavior and consistent convergence in a distributed environment. We show how the agent decision making process is enhanced by the combination of the two techniques in a distributed environment. Furthermore, path retracing is a challenging problem in a distributed environment, since the agent does not have complete knowledge of the environment. We propose a backtracking technique to keep the distributed agent informed all the time of its path information and step count including when migrating from one node to another. Note that no node has knowledge of the entire global path from a source to a goal when such a goal resides on a separate node. Each agent has only knowledge of a partial path (internal to a node) and related number of steps corresponding to the portion of the path that agent traversed when running on the node. In particular, we show how each of the agents(s), starting in one of the many sectors with no initial knowledge of the environment, using the proposed distributed technique, develops its intelligence based on its experience and seamlessly discovers the shortest global path to the target, which is located in a different node, while avoiding any obstacle(s) it encounters in its way, including when transitioning and migrating from one distributed computing node to another. The agent(s) use (s) multiple-token-ring message passing interface (MPI) to perform internode communication. Finally, the experimental results of the proposed method show that single and multiagents sharing the same goal and running on the same or different nodes successfully coordinate the sharing of their respective environment states/information to collaboratively perform their respective tasks. The results also show that distributed multiagent sharing information increases by an order of magnitude the speed of convergence to the optimal shortest path to the goal in comparison with the single-agent case or noninformation sharing multiagent case.
机译：本文提出了一种位于非结构化网络分布式环境中的自治代理的路径规划技术，其中每个代理对环境的了解有限且不完全。每个代理仅具有在其上运行的计算节点的分布式存储器中可用的知识，并且代理共享通过分布式网络学习的一些信息。特别地，环境被分为几个扇区，每个扇区位于单个单独的分布式计算节点上。我们考虑混合反应-认知智能体，其中我们使用自主智能体运动计划，该计划基于势场模型的使用以及强化学习以及边界检测算法。势场用于在分布式环境中快速收敛到路径，而强化学习用于保证分布式环境中的各种行为和一致收敛。我们展示了如何通过在分布式环境中结合两种技术来增强代理决策过程。此外，由于代理不完全了解环境，因此路径跟踪在分布式环境中是一个具有挑战性的问题。我们提出一种回溯技术，以使分布式代理始终知道其路径信息和步数，包括何时从一个节点迁移到另一个节点。请注意，当目标位于单独的节点上时，没有节点知道从源到目标的整个全局路径。每个代理仅了解部分路径（在节点内部）以及与代理在节点上运行时遍历的路径部分相对应的相关步骤数。特别是，我们展示了如何利用提议的分布式技术，从许多对环境没有初步了解的部门中的一个开始，从对环境没有初步了解的每个代理商，开发其基于其经验的情报，并无缝地发现最短的全球路径。目标位于不同的节点中，同时避免了它以其方式遇到的任何障碍，包括从一个分布式计算节点转换和迁移到另一个节点时。代理使用多令牌环消息传递接口（MPI）来执行节点间通信。最后，该方法的实验结果表明，具有相同目标并在相同或不同节点上运行的单个和多个代理成功地协调了各自环境状态/信息的共享，以共同执行各自的任务。结果还表明，与单代理案例或非信息共享多代理案例相比，分布式多代理共享信息的收敛速度提高了达到目标的最佳最短路径的收敛速度。

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