Before revision:  By doing so, we  bridge the gap from NAS research in supervised learning settings to the reinforcement learning scenario through this relatively simple marriage between two different  lines of research , and are one of the first to apply controller-based NAS techniques to RL . We demonstrate the utility  of our method by training combinatorial neural network architectures for RL problems in continuous control, via edge pruning and weight sharing .
After revision:  By doing so, we 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 , and are one of the first to apply controller-based NAS techniques to RL . We demonstrate the utility of our method by training combinatorial neural network architectures for RL problems in continuous control, via edge pruning and weight sharing .