文本游戏中基于强化学习的智能体设计与优化
Design and Optimization of Reinforcement Learning-Based Agents in Text-Based Games
AI总结：
针对文本游戏智能体游玩任务，提出结合深度学习世界模型与策略梯度深度强化学习的智能体设计新方法，在游戏完成率、胜率上显著优于现有智能体，为相关研究提供实证基础与通用优化方向。
摘要：
随着人工智能技术的发展，利用智能体游玩文本游戏的研究日渐流行。本文提出了一种在强化学习语境下的智能体设计与学习新方法。首先采用深度学习模型处理游戏文本并构建世界模型，随后通过基于策略梯度的深度强化学习方法训练智能体，实现从状态值到最优策略的转换。改进后的智能体在多项文本游戏实验中表现更优，在游戏完成率和胜率上显著超越此前的智能体。本研究为强化学习应用于文本游戏提供了新的认知与实证基础，也为更通用领域和问题的强化学习智能体开发与优化铺垫了道路。
英文摘要：
As AI technology advances, research in playing text-based games with agents has becomeprogressively popular. In this paper, a novel approach to agent design and agent learning ispresented with the context of reinforcement learning. A model of deep learning is first applied toprocess game text and build a world model. Next, the agent is learned through a policy gradient-based deep reinforcement learning method to facilitate conversion from state value to optimal policy.The enhanced agent works better in several text-based game experiments and significantlysurpasses previous agents on game completion ratio and win rate. Our study introduces novelunderstanding and empirical ground for using reinforcement learning for text games and sets thestage for developing and optimizing reinforcement learning agents for more general domains andproblems.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
