Layer-dependent excellent thermoelectric materials: from monolayer to trilayer tellurium based on DFT calculation.

Monoelemental two-dimensional (2D) materials, which are superior to binary and ternary 2D materials, currently attract remarkable interest due to their fascinating properties. Though the thermal and thermoelectric (TE) transport properties of tellurium have been studied in recent years, there is little research about the thermal and TE properties of multilayer tellurium with interlayer interaction force. Herein, the layer modulation of the phonon transport and TE performance of monolayer, bilayer, and trilayer tellurium is investigated by first-principles calcuations. First, it was found that thermal conductivity as a function of layer numbers possesses a robust, unusually non-monotonic behavior. Moreover, the anisotropy of the thermal transport properties of tellurium is weakened with the increase in the number of layers. By phonon-level systematic analysis, we found that the variation of phonon transport under the layer of increment was determined by increasing the phonon velocity in specific phonon modes. Then, the TE transport properties showed that the maximum figure of merit (ZT) reaches 6.3 (p-type) along the armchair direction at 700 K for the monolayer and 6.6 (p-type) along the zigzag direction at 700 K for the bilayer, suggesting that the TE properties of the monolayer are highly anisotropic. This study reveals that monolayer and bilayer tellurium have tremendous opportunities as candidates in TE applications. Moreover, further increasing the layer number to 3 hinders the improvement of TE performance for 2D tellurium.

A hierarchical reinforcement learning method for missile evasion and guidance.

This paper proposes an algorithm for missile manoeuvring based on a hierarchical proximal policy optimization (PPO) reinforcement learning algorithm, which enables a missile to guide to a target and evade an interceptor at the same time. Based on the idea of task hierarchy, the agent has a two-layer structure, in which low-level agents control basic actions and are controlled by a high-level agent. The low level has two agents called a guidance agent and an evasion agent, which are trained in simple scenarios and embedded in the high-level agent. The high level has a policy selector agent, which chooses one of the low-level agents to activate at each decision moment. The reward functions for each agent are different, considering the guidance accuracy, flight time, and energy consumption metrics, as well as a field-of-view constraint. Simulation shows that the PPO algorithm without a hierarchical structure cannot complete the task, while the hierarchical PPO algorithm has a 100% success rate on a test dataset. The agent shows good adaptability and strong robustness to the second-order lag of autopilot and measurement noises. Compared with a traditional guidance law, the reinforcement learning guidance law has satisfactory guidance accuracy and significant advantages in average time and average energy consumption.

Research on reinforcement learning-based safe decision-making methodology for multiple unmanned aerial vehicles.

A system with multiple cooperating unmanned aerial vehicles (multi-UAVs) can use its advantages to accomplish complicated tasks. Recent developments in deep reinforcement learning (DRL) offer good prospects for decision-making for multi-UAV systems. However, the safety and training efficiencies of DRL still need to be improved before practical use. This study presents a transfer-safe soft actor-critic (TSSAC) for multi-UAV decision-making. Decision-making by each UAV is modeled with a constrained Markov decision process (CMDP), in which safety is constrained to maximize the return. The soft actor-critic-Lagrangian (SAC-Lagrangian) algorithm is combined with a modified Lagrangian multiplier in the CMDP model. Moreover, parameter-based transfer learning is used to enable cooperative and efficient training of the tasks to the multi-UAVs. Simulation experiments indicate that the proposed method can improve the safety and training efficiencies and allow the UAVs to adapt to a dynamic scenario.