Reviews: Simple random search of static linear policies is competitive for reinforcement learning

The main idea is to demonstrate the effectiveness of these simple algorithms compared to the much more complex state-of-the-art RL algorithms proposed and evaluated on MuJoCo tasks. The results of the empirical evaluation are startling. The paper convincingly demonstrates very strong performance of the simple algorithm and policy class on the MuJoCo tasks. The evaluation is extremely thorough, the results are compelling and raise serious questions about the current state of RL algorithm evaluation methodology using MuJoCo. In my opinion, this paper is an excellent contribution to the RL literature.