## 2 Related Works
The policy is a deep neural network (DNN) that maps directly between raw sensor measurements to a differential robot's linear and rotational velocities in order to drive the robot towards its goal position. The policy network is decentralized in a way that each robot has a copy of the network. At each time step, the policy network receives an observation that consists of the readings of a 2D laser scanner, the goal's position relative to the robot, and the robot's current linear and rotational velocities. The action space from which the policy network samples the output is a continuous space in which the linear velocity is in the range between \([0.0,1.0]\) and the rotational velocity is in the range between \([-1.0,1.0]\). Hence, the problem is formulated as a partially observable Markov decision process (POMDP) [17]. They trained their neural network using proximal policy optimization (PPO) [16]. They adapted the centralized learning, decentralized execution paradigm in which each robot collects its own experiences (observations, actions, rewards) and the policy is trained on the collective experiences gathered by all the robots. It is obvious from the paper that it is similar to the approach used by [PERSON] et al. [10] in which the policy network effectively preforms navigation with both decentralized MPP and path tracking when the goal positions are set for each robot, but the paper did not address the MRTA problem. [PERSON] et al. [13] published subsequent work to [PERSON] et al. 's [12] work, in which they validated [PERSON] et al. 's policy in real-world setups. They made two observations. The first is that [PERSON] et al. 's policy does not perform optimally in some trivial situations in which there are no obstacles or other agents nearby or when a robot is near its goal position. The second is that although [PERSON] et al. 's policy has shown a very high success rate, it is still prone to collisions even though these collision events are rare. Thus, [PERSON] et al. proposed integrating [PERSON] et al. 's policy into a hybrid control framework to further improve the policy's robustness and effectiveness. The hybrid control system is composed of three separate sub-policies. The first policy is a PID policy that is used in cases where there are no obstacles or other agents nearby or the goal position is very close to the robot's current position. The PID policy provides an almost optimal solution to move the robot towards the goal position. The second policy is called the conservative safe policy, which is used in cases when the robot is very close to other agents or obstacles. The conservative safe policy uses [PERSON] et al. 's policy, but scales down the laser scanner input and sets a maximum velocity to the policy's output. The third policy is the same as [PERSON] et al. 's policy and is used in cases that are different from that of the other two sub-policies. The results show that the hybrid policy managed to navigate an MRS in a warehouse-like environment with human workers around. The policy also managed to navigate a single robot safely in a dense human crowd. Yet, it did not address the MRTA problem. In summary, a review of some works from the literature that used DRL methods either to solve the MRTA problem or the navigation problem is given. Table 1 shows a summary of which works from the literature performed MRTA and which performed navigation and compares them with our proposed method. As far as we know, there has not been an attempt to combine both MRTA and navigation in a single algorithm.