一种基于强化学习的学习Agent

强化学习通过感知环境状态和从环境中获得不确定奖赏值来学习动态系统的最优行为策略，是构造智能Agent的核心技术之一，在面向Agent的开发环境AODE中扩充BDI模型，引入策略和能力心智成分，采用强化学习技术实现策略构造函数，从而提出一种基于强化学习技术的学习Agent，研究AODE中自适应Agent物结构和运行方式，使智能Agent具有动态环境的在线学习能力，有效期能够有效地满足Agent各种心智要求。

A LEARNING AGENT BASED ON REINFORCEMENT LEARNING

Reinforcement learning can find optimal behavior sequence and perform on-line learning in dynamic environments by means of non-decisive rewards. Therefore reinforcement learning is one of the basic technologies of intelligent agent. The BDI model is extended, bringing strategy and capability into mental state, and strategy construct function is studied by means of reinforcement learning technology in an agent oriented development environment for intelligent software system, named AODE. An adaptive agent based on reinforcement learning is brought forward and a strategy construct function is realized. In this paper, an adaptive agent's architecture is studied deeply so that agent is able to learn on-line in dynamical surroundings and satisfy every mental requirements of agent effectively.