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The control of a mobile robot is a well-studied problem in robotics that can be solved easily when performing simple tasks without uncertainties. However, uncertainties always exist when a mobile robot performs a series of tasks in the real-world. In this paper, we proposed a reinforcement learning (RL)-based controller for a mobile robot to perform a task with uncertainties.We consider the case where a task consists of several subtasks described by syntactically co-safe linear temporal logic (scLTL) specifications and each scLTL specification is transformed into a finite state automaton (FSA) that accepts all behavior satisfying the scLTL specification. A reinforcement learning with an FSA-encoder (RLwF) method is proposed to learn an optimal control policy in performing tasks in an environment with uncertainties. We propose an RL-based control method with uncertainties to learn rapidly an optimal policy against the uncertainties such as jammers that prevent a mobile robot from performing specified tasks. By simulation, we demonstrate that the proposed controller can learn an optimal policy and generate an optimal path to perform the designed tasks.

Self-organization has potential for high scalability, adaptability, flexibility, and robustness, which are vital features for realizing future networks. Convergence of self-organizing control, however, is slow in some practical applications compared to control with conventional deterministic systems using global information. It is therefore important to facilitate convergence of self-organizing controls. In controlled self-organization, which introduces an external controller into self-organizing systems, the network is controlled to guide systems to a desired state. Although existing controlled self-organization schemes could achieve this feature, convergence speed for reaching an optimal or semioptimal solution is still a challenging task. We perform potential-based self-organizing routing and propose an optimal feedback method using a reduced-order model for faster convergence at low cost. Simulation results show that the proposed mechanism improves the convergence speed of potential-field construction (i.e., route construction) by at most 22.6 times with low computational and communication cost.