An agent may interact with its environment and learn complex tasks based on evaluative feedback through a process known as reinforcement learning. Reinforcement learning requires exploration of unfamiliar situations, which necessarily involves unknown and potentially dangerous or costly outcomes. Supervising agents in these situations can be seen as a type of nurturing and requires an investment of time usually by humans. Nurturing, one individual investing in the development of another individual with which it has an ongoing relationship, is widely seen in the biological world, often with parents nurturing their o spring. There are many types of nurturing, including helping an individual to carry out a task by doing part of the task for it. In arti cial intelligence, nurturing can be seen as an opportunity to develop both better machine learning algorithms and robots that assist or supervise other robots. Although the area of nurturing robotics is at a very early stage, the hope is that this approach can result in more sophisticated learning systems. This dissertation demonstrates the e ectiveness of nurturing through experiments involving the evolution of the parameters of a reinforcement learning algorithm that is capable of nding good policies in a changing environment in which the agent must learn an episodic task in which there is discrete input with perceptual aliasing, continuous output, and delayed reward. The results show that nurturing is capable of promoting the evolution of learning in such environments.