  Evolutionary Computation (EC) has been shown to be able to quickly train Deep
Artificial Neural Networks (DNNs) to solve Reinforcement Learning (RL)
problems. While a Genetic Algorithm (GA) is well-suited for exploiting reward
functions that are neither deceptive nor sparse, it struggles when the reward
function is either of those. To that end, Novelty Search (NS) has been shown to
be able to outperform gradient-following optimizers in some cases, while
under-performing in others. We propose a new algorithm: Explore-Exploit
$\gamma$-Adaptive Learner ($E^2\gamma AL$, or EyAL). By preserving a
dynamically-sized niche of novelty-seeking agents, the algorithm manages to
maintain population diversity, exploiting the reward signal when possible and
exploring otherwise. The algorithm combines both the exploitation power of a GA
and the exploration power of NS, while maintaining their simplicity and
elegance. Our experiments show that EyAL outperforms NS in most scenarios,
while being on par with a GA -- and in some scenarios it can outperform both.
EyAL also allows the substitution of the exploiting component (GA) and the
exploring component (NS) with other algorithms, e.g., Evolution Strategy and
Surprise Search, thus opening the door for future research.
