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. GRAPHICAL ABSTRACT

中文翻译：

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使用协作多智能体强化学习框架的多个迷你机器人导航

在这项工作中，我们研究了强化学习 (RL) 框架在多智能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，这些电机由一个简单高效的低级速度控制器控制。RL 代理的动作是每个迷你机器人的方向，它基于每个迷你机器人的位置、它们之间的距离以及每个迷你机器人与最近的机器人之间的距离梯度的符号。每个迷你机器人都被认为是其他人必须避开的移动障碍物。我们提出了合适的状态空间和奖励函数，从而形成了一个高效的协作 RL 框架。采用经典和双 Q 学习算法，其中后者被认为是学习迷你机器人的最佳策略，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态模型也包括在内。模拟了几个应用场景，并给出了结果来证明所提出方法的性能。图形概要 模拟了几个应用场景，并给出了结果来证明所提出方法的性能。图形概要 模拟了几个应用场景，并给出了结果来证明所提出方法的性能。图形概要