  We outline a way for an agent to learn the dispositions of a particular
individual through inverse reinforcement learning where the state space at time
t includes an fMRI scan of the individual, to represent his brain state at that
time. The fundamental assumption being that the information shown on an fMRI
scan of an individual is conditioned on his thoughts and thought processes. The
system models both long and short term memory as well any internal dynamics we
may not be aware of that are in the human brain. The human expert will put on a
suit for a set duration with sensors whose information will be used to train a
policy network, while a generative model will be trained to produce the next
fMRI scan image conditioned on the present one and the state of the
environment. During operation the humanoid robots actions will be conditioned
on this evolving fMRI and the environment it is in.
