论文标题
使用深Q学习的自主仓库机器人
Autonomous Warehouse Robot using Deep Q-Learning
论文作者
论文摘要
在仓库中，专门的代理需要导航，避免障碍并最大程度地利用仓库环境中的空间。由于这些环境的不可预测性，可以应用强化学习方法来完成这些任务。在本文中，我们建议使用深度加固学习（DRL）来解决机器人导航和避免障碍问题和传统Q学习，并具有较小的变化，以最大程度地利用空间用于产品放置。我们首先研究了单个机器人案例的问题。接下来，基于单个机器人模型，我们将系统扩展到多机器人案例。我们使用Q-表的战略变化来执行多代理Q学习。我们在2D模拟环境中成功地测试了单个和多机器人案例的模型性能。
In warehouses, specialized agents need to navigate, avoid obstacles and maximize the use of space in the warehouse environment. Due to the unpredictability of these environments, reinforcement learning approaches can be applied to complete these tasks. In this paper, we propose using Deep Reinforcement Learning (DRL) to address the robot navigation and obstacle avoidance problem and traditional Q-learning with minor variations to maximize the use of space for product placement. We first investigate the problem for the single robot case. Next, based on the single robot model, we extend our system to the multi-robot case. We use a strategic variation of Q-tables to perform multi-agent Q-learning. We successfully test the performance of our model in a 2D simulation environment for both the single and multi-robot cases.
