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 lines of research , and are one of the first to apply controller-based NAS techniques to RL . We demonstrate the utility 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 weight sharing .