In this work we investigate the use of a reinforcement learning (RL) framework for the autonomous navigation of a group of mini-robots in a multi-agent collaborative environment. Each mini-robot is driven by inertial forces provided by two vibration motors that are controlled by a simple and efficient low-level speed controller. The action of the RL agent is the direction of each mini-robot, and it is based on the position of each mini-robot, the distance between them and the sign of the distance gradient between each mini-robot and the nearest one. Each mini-robot is considered a moving obstacle that must be avoided by the others. We propose suitable state space and reward function that result in an efficient collaborative RL framework. The classical and the double Q-learning algorithms are employed, where the latter is considered to learn optimal policies of mini-robots that offers more stable and reliable learning process. A simulation environment is created, using the ROS framework, that include a group of four mini-robots. The dynamic model of each mini-robot and of the vibration motors is also included. Several application scenarios are simulated and the results are presented to demonstrate the performance of the proposed approach.

在这项工作中，我们调查了强化学习（RL）框架在多主体协作环境中用于一组微型机器人的自主导航的使用。每个微型机器人都由两个振动电机提供的惯性力驱动，该惯性力由一个简单而有效的低级速度控制器控制。RL代理的作用是每个微型机器人的方向，它基于每个微型机器人的位置，它们之间的距离以及每个微型机器人与最近机器人之间的距离梯度的符号。每个微型机器人都被视为移动障碍，其他机器人必须避免。我们提出合适的状态空间和奖励功能，以形成有效的协作RL框架。采用经典和双重Q学习算法，后者被认为是学习微型机器人的最佳策略，从而提供更稳定和可靠的学习过程。使用ROS框架创建了一个模拟环境，其中包括一组四个微型机器人。每个微型机器人和振动电机的动力学模型也包括在内。模拟了几种应用场景，并给出了结果来证明所提出方法的性能。