This study explores the decision making capabilities of Large Language Model (LLM) AI agents to automate learning in planetary landing missions. In particular, the work investigates the use of AI agents to minimise human intervention in training a lunar lander by providing high level strategic guidance to a Reinforcement Learning (RL) agent within the complex environment of Kerbal Space Program. To that end, LLM AI agents are utilised to interpret a lander manual and extract information for designing the rewards function of the RL algorithm, as well as to assess the training process and refine the rewards. A case study is conducted, comparing the performance of three types of LLMs, GPT-3.5-Turbo, GPT-4, and Meta-LLama-3-70B, in lunar landing tasks. The findings highlight the potential of interactive LLM agents for automated reward function generation and refinement, thus advancing AI capabilities in space exploration and autonomous navigation tasks.