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The increasing importance of robots and automation creates a demand forlearnable controllers which can be obtained through various approaches such asEvolutionary Algorithms (EAs) or Reinforcement Learning (RL). Unfortunately,these two families of algorithms have mainly developed independently and thereare only a few works comparing modern EAs with deep RL algorithms. We show thatMultidimensional Archive of Phenotypic Elites (MAP-Elites), which is a modernEA, can deliver better-performing solutions than one of the state-of-the-art RLmethods, Proximal Policy Optimization (PPO) in the generation of locomotioncontrollers for a simulated hexapod robot. Additionally, extensivehyper-parameter tuning shows that MAP-Elites displays greater robustness acrossseeds and hyper-parameter sets. Generally, this paper demonstrates that EAscombined with modern computational resources display promising characteristicsand have the potential to contribute to the state-of-the-art in controllerlearning.

Competitiveness of MAP-Elites against Proximal Policy Optimization on locomotion tasks in deterministic simulations