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.

提出采用深度强化学习和传统的 Q-learning 方法来解决仓库机器人导航，避障和产品摆放等问题，包括针对单机器人，多机器人的情况，成功在二维仿真环境中测试了模型的性能。

基于深度 Q 学习的自主仓库机器人

Autonomous Warehouse Robot using Deep Q-Learning

Connecting consumers with relevant products is a very important problem in
both online and offline commerce. In physical retail, product placement is an
effective way to connect consumers with products. However, selecting product
locations within a store can be a tedious process. Moreover, learning important
spatial patterns in offline retail is challenging due to the scarcity of data
and the high cost of exploration and experimentation in the physical world. To
address these challenges, we propose a stochastic model of spatial demand in
physical retail. We show that the proposed model is more predictive of demand
than existing baselines. We also perform a preliminary study into different
automation techniques and show that an optimal product allocation policy can be
learned through Deep Q-Learning.

本文介绍了一种基于随机模型的实体零售中的空间需求分析方法，此模型能够有效地预测产品需求并通过深度强化学习自主学习最优的产品摆放策略。