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A METHOD AND SYSTEM FOR IMPLEMENTING REINFORCEMENT LEARNING AGENT USING REINFORCEMENT LEARNING PROCESSOR

机译:使用强化学习处理器实现强化学习代理的方法和系统

摘要

The embodiments herein disclose a system and method foe implementing reinforcement learning agents using a reinforcement learning processor. As application-domain specific instruction set (ASI) for implementing reinforcement learning agents and reward functions is created. Further, instructions are created by including at least one of the reinforcement teaming agent ID vectors, the reinforcement learning environment ID vectors, and length of vector as an operand. The reinforcement learning agent ID vectors and the reinforcement learning environment ID vectors are pointers to a base address of an operations memory. Further, at least one of said reinforcement learning agent ID vector and reinforcement learning environment ID vector is embedded into operations associated with the decoded instruction. The instructions retrieved by agent II) vector indexed operation are executed using a second processor, and applied onto a 'group of reinforcement learning agents. The operations defined fay the instructions are stored in an operations storage memory.
机译:本文的实施例公开了一种使用强化学习处理器来实现强化学习代理的系统和方法。作为用于实施强化学习代理和奖励功能的特定于应用程序领域的指令集(ASI),已创建。进一步,通过包括强化分组代理ID矢量,强化学习环境ID矢量和矢量的长度中的至少一个作为操作数来创建指令。强化学习代理ID向量和强化学习环境ID向量是指向操作存储器的基地址的指针。此外,所述增强学习代理ID向量和增强学习环境ID向量中的至少一个被嵌入与解码的指令相关联的操作中。使用第二处理器执行由代理II)向量索引操作检索的指令,并将其应用于“一组强化学习代理”。指令定义的操作存储在操作存储存储器中。

著录项

  • 公开/公告号WO2018164740A1

    专利类型

  • 公开/公告日2018-09-13

    原文格式PDF

  • 申请/专利权人 ALPHAICS CORPORATION;

    申请/专利号WO2017US62785

  • 发明设计人 NAGARAJA NAGENDRA;

    申请日2017-11-21

  • 分类号G06F15/18;

  • 国家 WO

  • 入库时间 2022-08-21 12:42:45

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