  In response to the limitations of reinforcement learning and evolutionary
algorithms (EAs) in complex problem-solving, Evolutionary Reinforcement
Learning (EvoRL) has emerged as a synergistic solution. EvoRL integrates EAs
and reinforcement learning, presenting a promising avenue for training
intelligent agents. This systematic review firstly navigates through the
technological background of EvoRL, examining the symbiotic relationship between
EAs and reinforcement learning algorithms. We then delve into the challenges
faced by both EAs and reinforcement learning, exploring their interplay and
impact on the efficacy of EvoRL. Furthermore, the review underscores the need
for addressing open issues related to scalability, adaptability, sample
efficiency, adversarial robustness, ethic and fairness within the current
landscape of EvoRL. Finally, we propose future directions for EvoRL,
emphasizing research avenues that strive to enhance self-adaptation and
self-improvement, generalization, interpretability, explainability, and so on.
Serving as a comprehensive resource for researchers and practitioners, this
systematic review provides insights into the current state of EvoRL and offers
a guide for advancing its capabilities in the ever-evolving landscape of
artificial intelligence.
