There is a recent surge in interest for imitation learning, with large human
video-game and robotic manipulation datasets being used to train agents on very
complex tasks. While deep neuroevolution has recently been shown to match the
performance of gradient-based techniques on various reinforcement learning
problems, the application of deep neuroevolution techniques to imitation
learning remains relatively unexplored. In this work, we propose to explore
whether deep neuroevolution can be used for behaviour imitation on popular
simulation environments. We introduce a simple co-evolutionary adversarial
generation framework, and evaluate its capabilities by evolving standard deep
recurrent networks to imitate state-of-the-art pre-trained agents on 8 OpenAI
Gym state-based control tasks. Across all tasks, we find the final elite actor
agents capable of achieving scores as high as those obtained by the pre-trained
agents, all the while closely following their score trajectories. Our results
suggest that neuroevolution could be a valuable addition to deep learning
techniques to produce accurate emulation of behavioural agents. We believe that
the generality and simplicity of our approach opens avenues for imitating
increasingly complex behaviours in increasingly complex settings, e.g. human
behaviour in real-world settings. We provide our source code, model checkpoints
and results at github.com/MaximilienLC/gane.

本研究探索深度神经进化算法是否可用于行为模仿，通过引入一个简单的对抗生成框架，将深度递归网络进化到可以在 8 个 OpenAI Gym 状态控制任务上模仿最先进的预训练代理的能力。实验结果表明，神经进化在生成行为模拟代理方面具有很大的潜力，可以使其在准确度和泛化性方面更具可操作性。