  Developing agents that can perform challenging complex tasks is the goal of
reinforcement learning. The model-free reinforcement learning has been
considered as a feasible solution. However, the state of the art research has
been to develop increasingly complicated techniques. This increasing complexity
makes the reconstruction difficult. Furthermore, the problem of reward
dependency is still exists. As a result, research on imitation learning, which
learns policy from a demonstration of experts, has begun to attract attention.
Imitation learning directly learns policy based on data on the behavior of the
experts without the explicit reward signal provided by the environment.
However, imitation learning tries to optimize policies based on deep
reinforcement learning such as trust region policy optimization. As a result,
deep reinforcement learning based imitation learning also poses a crisis of
reproducibility. The issue of complex model-free model has received
considerable critical attention. A derivative-free optimization based
reinforcement learning and the simplification on policies obtain competitive
performance on the dynamic complex tasks. The simplified policies and
derivative free methods make algorithm be simple. The reconfiguration of
research demo becomes easy. In this paper, we propose an imitation learning
method that takes advantage of the derivative-free optimization with simple
linear policies. The proposed method performs simple random search in the
parameter space of policies and shows computational efficiency. Experiments in
this paper show that the proposed model, without a direct reward signal from
the environment, obtains competitive performance on the MuJoCo locomotion
tasks.
