Nature provides a way to understand physics with reinforcement learning since nature favors the economical way for an object to propagate. In the case of classical mechanics, nature favors the object to move along the path according to the integral of the Lagrangian, called the action S. We consider setting the reward/penalty as a function of S, so the agent could learn the physical trajectory of particles in various kinds of environments with reinforcement learning. In this work, we verified the idea by using a Q-Learning based algorithm on learning how light propagates in materials with different refraction indices, and show that the agent could recover the minimal-time path equivalent to the solution obtained by Snell's law or Fermat's Principle. We also discuss the similarity of our reinforcement learning approach to the path integral formalism. * equal contribution Preprint. Comments welcome.

The Internet of Things (IoT) technology is widely used and has been improved in research. However, due to the extensiveness of IoT technology, the heterogeneity and diversity of the device structure, the number of attacks against IoT has increased dramatically, so we need a method that can effectively and actively determine safety. Considering the diversity of the terminal structure of IoT, a security method for the IoT terminal based on structural balance, method objectivity, and reliability is currently a challenging task. This paper introduces the idea of rate of change in mathematics into trust analysis, and forms three attribute sets based on trust interval and rate of change: discrete interval, change range, and change frequency. By calculating the above attributes of the entity’s trust value, the entity’s trust situation is obtained, and an overall assessment of the terminal entity’s trust situation is made from the three levels of completeness, accuracy and objectivity. Under the premise of reducing encryption and other means, the above method can evaluate the trust state of the IoT terminal from the perspective of the data, and this evaluation method can provide a basis for the judgment of the IoT terminal more objectively and accurately.

Metal oxide semiconductors have attracted growing attention due to their excellent electrical and optical properties, low-cost fabrication, and good stability, which grant them great application potential in photodetectors (PDs). However, broadband photoelectrochemical (PEC)-type PDs based on metal oxide semiconductors are deficient. In this work, we synthesized twodimensional (2D) CuO nanosheets (NSs) by annealing Cu-based metal−organic frameworks (Cu-MOFs) and first demonstrated broadband PEC PDs based on 2D CuO NSs. The thicknesses of 2D CuO NSs are 2−6 nm. 2D CuO PEC PDs show a broadband photoresponse from 365 to 850 nm with a responsivity of 2.7 mA/ W and a response time of 60/400 ms, which surpass most 2D material-based PEC PDs. Moreover, CuO NSs PEC PDs exhibit outstanding multicycle stability and long-term stability for 1-month storage with negligible decay, exceeding all recently reported 2D material-based PEC PDs. These results endow 2D CuO NSs with great potential in underwater photodetection.

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