The ability to detect and monitor dynamic ocean features is crucial for understanding and mitigating environmental impacts. These features, which can include variations in temperature, salinity, and biological phenomena, play a significant role in marine ecosystems and can have substantial ecological and economic consequences. This thesis explores the application of Reinforcement Learning (RL) for monitoring ocean features through the simulation of an autonomous agent operating in a dynamic and uncertain environment. The work builds on a pre-existing project, modifying the environment to integrate various ocean features and implementing advanced detection techniques supported by sensors and an optimized reward function to guide the agent’s behavior.