## 2 Related Work  
Learning mobile robot policies involving DRL has been an active field since the recent rise in DL technology. Multiple DRL architectures have been developed for training policies conditioned directly on sensor data [4; 7]. Deep Q-network (DQN) methods have been used to help robots learn navigation in discreet action spaces from camera as well as laser data [8; 9; 10; 11]. Here, a set of actions is presented to a neural network, and the Q-value is estimated for each of them. The network learns a proper value estimation for each action, and one of them is selected for execution based on the designed criteria. However, navigation is conditioned on a specified discrete set of actions, which may be a rough approximation of robot motion. For robot navigation in a continuous-action space, actor-critic networks are often used. Soft actor-critic (SAC) is used for the same task in [12; 13; 14] with the ability to obtain smooth actions within a specified range. Deep deterministic policy gradient (DDPG) and its extension twin delayed deep deterministic policy gradient (TD3) have been successfully applied to navigation tasks with smooth navigation controls [15; 16; 17; 18]. Here, state representations are given to a neural network that consists of two parallel structures: the actor and the critic. The actor network part calculates the output actions in the form of real scalars, and the critic evaluates the value of this state action pair. However, while these approaches are successful for learning single robot navigation policies, they do not consider learned interaction strategies in shared environments. That is to say, they consider only their internal state and direct observations of the environment for their motion policy. Collaborative robot navigation is often solved in the federated and swarm robotics field [19; 20]. In the case of federated robot control, there exists a requirement for a central system that designates tasks and commands to subordinate robots [21]. In such a case, there is no hard requirement for information exchange between the robots themselves or specific intelligent interaction modeling. But there is a requirement for a server system that supervises all the tasks [22]. Swarm robotics requires the exchange of information through communication channels between agents in the swarm [23; 24; 25; 26]. In the floor-cleaning robot domain, direct exchange between scene agents is difficult and multiple devices perform different tasks. Besides, not all scene agents can be considered as part of a single swarm. Moreover, each scene agent needs to perform actions based only on its tasks and information while taking incoming information into account. In such cases, more planning-based approaches use a prior map or knowledge of the environment and devices to create an initial plan of action and update it according to motion rules in certain instances [27; 28; 29]. However, when dealing with unknown environments and scene agents that are not known before the task execution, other methods of information exchange need to be used. To facilitate explicit information exchange, we can form the agents in the scene and their interactions as a graph. Here, graph neural network (GNN) nomenclature can be used to define the tasks of the information exchange between the agents and the agents themselves. Agents can be considered as nodes and their relative state as the edge information [30; 31]. Using this topology, it is possible to obtain a node embedding that consists of agent information as well as the incoming information of the surrounding agents. This has been applied in the decentralized path planning domain, where all known and connected agents in the scene exchange relevant information to update each agent's information with their state [32; 33; 34]. This helps each agent to obtain a high-level plan for a more informed path. But for learning policies of individual agents, full-scene connectivity is often not available.