  The problem of Learning from Demonstration is targeted at learning to perform
tasks based on observed examples. One approach to Learning from Demonstration
is Inverse Reinforcement Learning, in which actions are observed to infer
rewards. This work combines a feature based state evaluation approach to
Inverse Reinforcement Learning with neuroevolution, a paradigm for modifying
neural networks based on their performance on a given task. Neural networks are
used to learn from a demonstrated expert policy and are evolved to generate a
policy similar to the demonstration. The algorithm is discussed and evaluated
against competitive feature-based Inverse Reinforcement Learning approaches. At
the cost of execution time, neural networks allow for non-linear combinations
of features in state evaluations. These valuations may correspond to state
value or state reward. This results in better correspondence to observed
examples as opposed to using linear combinations. This work also extends
existing work on Bayesian Non-Parametric Feature Construction for Inverse
Reinforcement Learning by using non-linear combinations of intermediate data to
improve performance. The algorithm is observed to be specifically suitable for
a linearly solvable non-deterministic Markov Decision Processes in which
multiple rewards are sparsely scattered in state space. A conclusive
performance hierarchy between evaluated algorithms is presented.
