  Reinforcement learning (RL) problems often feature deceptive local optima,
and learning methods that optimize purely for reward signal often fail to learn
strategies for overcoming them. Deep neuroevolution and novelty search have
been proposed as effective alternatives to gradient-based methods for learning
RL policies directly from pixels. In this paper, we introduce and evaluate the
use of novelty search over agent action sequences by string edit metric
distance as a means for promoting innovation. We also introduce a method for
stagnation detection and population resampling inspired by recent developments
in the RL community that uses the same mechanisms as novelty search to promote
and develop innovative policies. Our methods extend a state-of-the-art method
for deep neuroevolution using a simple-yet-effective genetic algorithm (GA)
designed to efficiently learn deep RL policy network weights. Experiments using
four games from the Atari 2600 benchmark were conducted. Results provide
further evidence that GAs are competitive with gradient-based algorithms for
deep RL. Results also demonstrate that novelty search over action sequences is
an effective source of selection pressure that can be integrated into existing
evolutionary algorithms for deep RL.
