Competitiveness of MAP-Elites against Proximal Policy Optimization on
  locomotion tasks in deterministic simulations
  The increasing importance of robots and automation creates a demand for
learnable controllers which can be obtained through various approaches such as
Evolutionary Algorithms (EAs) or Reinforcement Learning (RL). Unfortunately,
these two families of algorithms have mainly developed independently and there
are only a few works comparing modern EAs with deep RL algorithms. We show that
Multidimensional Archive of Phenotypic Elites (MAP-Elites), which is a modern
EA, can deliver better-performing solutions than one of the state-of-the-art RL
methods, Proximal Policy Optimization (PPO) in the generation of locomotion
controllers for a simulated hexapod robot. Additionally, extensive
hyper-parameter tuning shows that MAP-Elites displays greater robustness across
seeds and hyper-parameter sets. Generally, this paper demonstrates that EAs
combined with modern computational resources display promising characteristics
and have the potential to contribute to the state-of-the-art in controller
learning.
