  Imitation learning demonstrates remarkable performance in various domains.
However, imitation learning is also constrained by many prerequisites. The
research community has done intensive research to alleviate these constraints,
such as adding the stochastic policy to avoid unseen states, eliminating the
need for action labels, and learning from the suboptimal demonstrations.
Inspired by the natural reproduction process, we proposed a method called GenIL
that integrates the Genetic Algorithm with imitation learning. The involvement
of the Genetic Algorithm improves the data efficiency by reproducing
trajectories with various returns and assists the model in estimating more
accurate and compact reward function parameters. We tested GenIL in both Atari
and Mujoco domains, and the result shows that it successfully outperforms the
previous extrapolation methods over extrapolation accuracy, robustness, and
overall policy performance when input data is limited.

Neural and Evolutionary Computing;Machine Learning