## 1 Introduction  
In recent years, AI has enabled machines to perform tasks typically requiring human intelligence including visual perception, speech recognition, decision-making, and language translation, transforming numerous fields [1, 2, 3]. Among AI's many applications, machine learning (ML) stands out, enabling algorithms to learn from data and make predictions or decisions without explicit programming [4]. A further specialization within the field of machine learning is deep learning (DL), which utilizes neural networks with multiple layers to model intricate patterns in extensive datasets [5, 6, 7, 8]. Deep learning algorithms, particularly those involving reinforcement learning, have shown great promise in enabling agents to develop sophisticated strategies through trial and error [9, 10]. By leveraging neural networks, these agents can process vast amounts of sensory data, identify patterns, and make informed decisions. This is particularly beneficial in scenarios where agents must operate collaboratively to achieve a common goal [11]. The integration of DL with multi-agent systems has seen a marked increase in interest recently [12, 13], particularly in the context of mobile robot formation control [14]. This innovative approach utilizes the power of DL to enable multiple autonomous agents to learn and adapt their behaviours by interacting with their environment and each other [15]. The primary objective is to achieve and maintain specific formations, crucial for applications ranging from unmanned aerial vehicle (UAV) swarms to autonomous vehicle platoons [16; 17]. By addressing the complexities of coordination, communication, and dynamic environments, DL offers a promising solution to enhance the efficiency and robustness of multi-robot systems. During the last few years, our research has focused on the position control of mobile robots [18], leveraging advanced deep reinforcement learning algorithms. Our previous work [19; 20] details the design, development, and implementation of reinforcement learning algorithms for controlling the position of a wheeled mobile robot Khepera IV. These approaches facilitate the learning of optimal control policies through environmental interaction, resulting in significantly improved performance compared with traditional control methods [21; 22; 23; 24]. Our experiments, conducted both in simulation and real-world environments, demonstrate the effectiveness of our algorithms in achieving precise position control, even in the presence of obstacles. This work presents innovative models that enable autonomous agents to learn and adapt their behaviours using deep reinforcement learning (DRL). These models show the potential of this approach in applications such as UAV swarms and autonomous vehicle platoons. This research addresses the complexities of coordination and communication required to maintain precise formations. Using DRL, the models allow multiple agents to process large amounts of data in real-time. This enables them to make better decisions and adjust their positions dynamically. This capability ensures the agents can operate cohesively, maintaining their formation even in dynamic and unpredictable environments. The enhanced coordination and communication lead to increased efficiency and robustness of multi-robot systems, making them more reliable and effective in various scenarios. This work's findings could significantly improve autonomous systems, from transportation and surveillance to other fields. The main contributions of this work are as follows:  
1. Design and implementation: The design and implementation of a control law for the formation position control of a group of robots based on DRL. This control law helps robots maintain precise formations and adapt to their surroundings, improving on the results presented in [18], where classical controllers were used. 2. Simulation environment: The implementation of the proposed algorithm in a simulation environment, including obstacle avoidance.