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Abstract
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.
Summary
Imagine a robot in a large warehouse that needs to find certain items tagged with RFID (radio-frequency identification) chips. Instead of wandering randomly or following a simple set of instructions, this robot employs a sophisticated reinforcement learning strategy capable of juggling different types of information (a “multi-state-space” approach). By thinking several steps ahead rather than settling for short-term gains, the robot can plan effectively over an extended time horizon to locate items more efficiently.
This paper addresses how such a robot can make these smart, long-term decisions while relying on RFID signals: by splitting the problem into multiple “spaces” (movement space, sensor space, etc.) and leveraging reinforcement learning, the robot learns to better plan its route and interpret noisy signals. The result is a more robust and efficient system that can handle large-scale searching tasks—locating items faster, even when they’re hidden or dispersed over a big area.
