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Intelligent traffic light control is one of the modern approaches to solve traffic congestion, where reinforcement learning is a widely used method. Conventionally, reinforcement learning is used to determine whether to change the current phase (or choose a traffic phase) after each small interval. One major drawback of these approaches is that it makes the current traffic light phase duration uncertain before the current phase terminates. Directly determining the duration of the traffic light phase can effectively avoid this shortcoming. An adaptive traffic light timing system is proposed in this paper which can directly control the phase duration. In the proposed system, the Q-learning algorithm is employed and the action space is defined as all possible phase durations. In addition, the reward function is redefined to guide the agent to balance more traffic metrics, and the state is redefined to reduce the state space. Finally, the proposed system is evaluated by equal, unequal, and complex traffic scenarios. Results show that the proposed system has a better performance compared with other methods in controlling traffic lights, even on complex traffic scenarios. [ABSTRACT FROM AUTHOR]

The formation of unmanned underwater vehicles (UUVs) has wide potential for applications in various marine activities. This paper studies the robust formation protocol design problem for multiple UUVs, whose dynamics are subject to nonlinearity, parametric uncertainties, and external disturbances. A robust distributed formation control scheme is proposed, which yields a control structure involving a position loop and an attitude loop to govern the translational motion and rotational motion, respectively. Theoretical analysis is given to show the robustness properties of the global closed-loop control system. Simulation results are provided to validate the effectiveness of the proposed formation control method. [ABSTRACT FROM AUTHOR]