基于rfid的机器人搜索与规划任务的多状态空间推理强化学习

Zhitao Yu;Jian Zhang;Shiwen Mao;Senthilkumar C. G. Periaswamy;Justin Patton
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摘要

近年来,强化学习(RL)在机器人应用中显示出了很高的潜力。然而,RL在很大程度上依赖于奖励函数,并且代理仅仅遵循策略来最大化奖励,而缺乏推理能力。因此,RL可能不适用于长期机器人任务。在本文中,我们提出了一种新的学习框架,称为多状态空间推理强化学习(SRRL),以赋予智能体主要的推理能力。首先,我们抽象了多个状态空间之间隐含和潜在的联系。然后,我们通过长短期记忆(LSTM)网络嵌入历史观察,以保存长期记忆和相关性。所提出的SRRL的抽象能力和长期记忆能力使代理能够通过利用射频识别(RFID)传感特性和环境占用图之间的相关性,更快、更合理地执行长期机器人搜索和规划任务。我们在基于视觉游戏的模拟环境中通过实验验证了SRRL的有效性。我们的方法显著优于三种最先进的基线方案。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Multi-State-Space Reasoning Reinforcement Learning for Long-Horizon RFID-Based Robotic Searching and Planning Tasks
In recent years, reinforcement learning (RL) has shown high potential for robotic applications. However, RL heavily relies on the reward function, and the agent merely follows the policy to maximize rewards but lacks reasoning ability. As a result, RL may not be suitable for long-horizon robotic tasks. In this paper, we propose a novel learning framework, called multiple state spaces reasoning reinforcement learning (SRRL), to endow the agent with the primary reasoning capability. First, we abstract the implicit and latent links between multiple state spaces. Then, we embed historical observations through a long short-term memory (LSTM) network to preserve long-term memories and dependencies. The proposed SRRL's ability of abstraction and long-term memory enables agents to execute long-horizon robotic searching and planning tasks more quickly and reasonably by exploiting the correlation between radio frequency identification (RFID) sensing properties and the environment occupation map. We experimentally validate the efficacy of SRRL in a visual game-based simulation environment. Our methodology outperforms three state-of-the-art baseline schemes by significant margins.
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