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
