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Obstacle avoidance is a fundamental requirement for autonomous robots whichoperate in, and interact with, the real world. When perception is limited tomonocular vision avoiding collision becomes significantly more challenging dueto the lack of 3D information. Conventional path planners for obstacleavoidance require tuning a number of parameters and do not have the ability todirectly benefit from large datasets and continuous use. In this paper, adueling architecture based deep double-Q network (D3QN) is proposed forobstacle avoidance, using only monocular RGB vision. Based on the dueling anddouble-Q mechanisms, D3QN can efficiently learn how to avoid obstacles in asimulator even with very noisy depth information predicted from RGB image.Extensive experiments show that D3QN enables twofold acceleration on learningcompared with a normal deep Q network and the models trained solely in virtualenvironments can be directly transferred to real robots, generalizing well tovarious new environments with previously unseen dynamic objects.

Towards Monocular Vision based Obstacle Avoidance through Deep Reinforcement Learning