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.
翻译:在仓库中,专门代理人需要导航、避免障碍和最大限度地利用仓库环境中的空间。由于这些环境的不可预测性,可以采用强化学习方法完成这些任务。在本文件中,我们提议使用深强化学习(DRL)来解决机器人导航和障碍避免问题,以及传统的Q学习,但略有改动,以最大限度地利用产品放置空间。我们首先调查单机器人案例的问题。接着,根据单一机器人模型,我们将我们的系统扩大到多机器人案例。我们使用多剂Q学习数据库的战略变异来进行多剂Q学习。我们成功地测试了我们模型在二维模拟环境中的单一和多机器人案例的性能。