A fast learning approach for autonomous navigation using a deep reinforcement learning method

Deep reinforcement learning‐based methods employ an ample amount of computational power that affects the learning process. This paper proposes a novel approach to speed up the training process and improve the performance of autonomous navigation for a tracked robot. The proposed model named “layer normalization dueling double deep Q‐network” has been trained in a virtual environment and then implemented it to a tracked robot for testing in a real‐world scenario. Depth images have been used instead of RGB images to preserve the temporal information. Features are extracted using convolutional neural networks, and actions are derived using the dueling double deep Q‐network. The input data has been normalized before each convolutional layer, which reduces the covariate shift by 69%. This end‐to‐end network architecture of the proposed model provides stability to the network, relieves the burden of computational cost, and converges in much less number of episodes. Compared with three Q‐variant models, the proposed model demonstrates outstanding performance in terms of episodic reward and convergence rate. The proposed model took 12.8% fewer episodes for training compared to other models.