Deep reinforcement learning (RL) has achieved important success on several complicated serial higher cognitive process issues. However, RL algorithms sometimes need a great deal of interactions with an surroundings to achieve smart performance. Initial performance could also be nearly random, clearly sub best, and infrequently rather dangerous in real world settings like autonomous driving.