  For the purpose of inspecting power plants, autonomous robots can be built
using reinforcement learning techniques. The method replicates the environment
and employs a simple reinforcement learning (RL) algorithm. This strategy might
be applied in several sectors, including the electricity generation sector. A
pre-trained model with perception, planning, and action is suggested by the
research. To address optimization problems, such as the Unmanned Aerial Vehicle
(UAV) navigation problem, Deep Q-network (DQN), a reinforcement learning-based
framework that Deepmind launched in 2015, incorporates both deep learning and
Q-learning. To overcome problems with current procedures, the research proposes
a power plant inspection system incorporating UAV autonomous navigation and DQN
reinforcement learning. These training processes set reward functions with
reference to states and consider both internal and external effect factors,
which distinguishes them from other reinforcement learning training techniques
now in use. The key components of the reinforcement learning segment of the
technique, for instance, introduce states such as the simulation of a wind
field, the battery charge level of an unmanned aerial vehicle, the height the
UAV reached, etc. The trained model makes it more likely that the inspection
strategy will be applied in practice by enabling the UAV to move around on its
own in difficult environments. The average score of the model converges to
9,000. The trained model allowed the UAV to make the fewest number of rotations
necessary to go to the target point.
