We introduce ES-ENAS, a simple yet general evolutionary joint optimization procedure by combining continuous optimization via Evolutionary Strategies (ES) and combinatorial optimization via Efficient NAS (ENAS) in a highly scalable and intuitive way. Our main insight is noticing that ES is already a highly distributed algorithm involving hundreds of forward passes which can not only be used for training neural network weights, but also for jointly training a NAS controller, both in a blackbox fashion. By doing so, we also bridge the gap from NAS research in supervised learning settings to the reinforcement learning scenario through this relatively simple marriage between two different yet common lines of research. We demonstrate the utility and effectiveness of our method over a large search space by training highly combinatorial neural network architectures for RL problems in continuous control, via edge pruning and quantization. We also incorporate a wide variety of popular techniques from modern NAS literature including multiobjective optimization along with various controller methods, to showcase their promise in the RL field and discuss possible extensions.
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