Effective lazy training method for deep q-network in obstacle avoidance and path planning

Deep reinforcement learning technique combines reinforcement learning and neural network for various applications. This paper is to propose an effective lazy training method for deep reinforcement learning, especially for deep Q-network combining neural network with Q-learning to be used for the obstacle avoidance and path planning applications. The proposed method can reduce the overall training time by designing a lazy learning method and a method removing unnecessary repetitions in the training step. These two methods can reduce a significant portion of total execution time without losing any required accuracy. The proposed method is evaluated for the obstacle avoidance and path planning tasks, where an agent trapped in an unknown environment is trying to find out the shortest path to the destination without any collision, through its self-study. And the experiment results show that the proposed method reduces 53.38% of training time on average, compared to the traditional method with no performance loss and make the training procedure more stable.