  In this work, we propose a novel approach for reinforcement learning driven
by evolutionary computation. Our algorithm, dubbed as Evolutionary-Driven
Reinforcement Learning (evo-RL), embeds the reinforcement learning algorithm in
an evolutionary cycle, where we distinctly differentiate between purely
evolvable (instinctive) behaviour versus purely learnable behaviour.
Furthermore, we propose that this distinction is decided by the evolutionary
process, thus allowing evo-RL to be adaptive to different environments. In
addition, evo-RL facilitates learning on environments with rewardless states,
which makes it more suited for real-world problems with incomplete information.
To show that evo-RL leads to state-of-the-art performance, we present the
performance of different state-of-the-art reinforcement learning algorithms
when operating within evo-RL and compare it with the case when these same
algorithms are executed independently. Results show that reinforcement learning
algorithms embedded within our evo-RL approach significantly outperform the
stand-alone versions of the same RL algorithms on OpenAI Gym control problems
with rewardless states constrained by the same computational budget.
