Armin Farhadi

Afsaneh Mollasalehi, Armin Farhadi

Energy crisis has forced many countries to think of a replacement for energy supply. Renewable energy sources as firendly environment sources play a pivotal role in producing clean energy for various sectors in industry. Gas emissions originating from the transportation industry is another contributing factor to air pollution. Hence, designing and utilizing vehicles that run on renewable energy is crucial, as it provides a dependable energy source that is naturally abundant, leaves nearly no carbon footprint, and is sustainable. Solar powered electric cars make a significant impact on global climate change. To better understand this impact and building upon the plenty of research done on this topic, this paper aims to provide a comprehensive review of the various factors related to solar cars. Specifically, this review will examine the following key factors: Types and sizing of solar cars, solar vehicle power source configurations, leading solar car nations, and solar car challenges.

Zhang Hengyu, Maryam Cheraghy, Liu Wei et al.

This paper proposes an Improved Noisy Deep Q-Network (Noisy DQN) to enhance the exploration and stability of Unmanned Aerial Vehicle (UAV) when applying deep reinforcement learning in simulated environments. This method enhances the exploration ability by combining the residual NoisyLinear layer with an adaptive noise scheduling mechanism, while improving training stability through smooth loss and soft target network updates. Experiments show that the proposed model achieves faster convergence and up to $+40$ higher rewards compared to standard DQN and quickly reach to the minimum number of steps required for the task 28 in the 15 * 15 grid navigation environment set up. The results show that our comprehensive improvements to the network structure of NoisyNet, exploration control, and training stability contribute to enhancing the efficiency and reliability of deep Q-learning.