  Recent successes combine reinforcement learning algorithms and deep neural
networks, despite reinforcement learning not being widely applied to robotics
and real world scenarios. This can be attributed to the fact that current
state-of-the-art, end-to-end reinforcement learning approaches still require
thousands or millions of data samples to converge to a satisfactory policy and
are subject to catastrophic failures during training. Conversely, in real world
scenarios and after just a few data samples, humans are able to either provide
demonstrations of the task, intervene to prevent catastrophic actions, or
simply evaluate if the policy is performing correctly. This research
investigates how to integrate these human interaction modalities to the
reinforcement learning loop, increasing sample efficiency and enabling
real-time reinforcement learning in robotics and real world scenarios. This
novel theoretical foundation is called Cycle-of-Learning, a reference to how
different human interaction modalities, namely, task demonstration,
intervention, and evaluation, are cycled and combined to reinforcement learning
algorithms. Results presented in this work show that the reward signal that is
learned based upon human interaction accelerates the rate of learning of
reinforcement learning algorithms and that learning from a combination of human
demonstrations and interventions is faster and more sample efficient when
compared to traditional supervised learning algorithms. Finally,
Cycle-of-Learning develops an effective transition between policies learned
using human demonstrations and interventions to reinforcement learning. The
theoretical foundation developed by this research opens new research paths to
human-agent teaming scenarios where autonomous agents are able to learn from
human teammates and adapt to mission performance metrics in real-time and in
real world scenarios.
