Lunar Lander Using Reinforcement Learning Algorithm

The harsh and dynamic lunar surface environment, developing autonomous lunar landing has proven to be a difficult problem for space agencies. Reinforcement learning (RL) has recently come to light as a viable solution to this issue. In this research, In this paper we proposed that an RL-based lunar lander control system that enables the lunar lander to travel and land on the moon on its own. The proposed paper comprises of a deep neural network that has been taught the best control strategy using RL algorithms. The lander may learn from its mistakes and gradually improve through the RL algorithm. The results of the simulations demonstrate that, even in the presence of uncertainties and disturbances, the proposed system can effectively lead the lunar lander to a secure landing on the lunar surface. The proposed method offers fresh options for space research and shows the potential of RL in creating autonomous control systems for lunar landers.