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A method called BOAE\u2010DDPG, which combines the novel bidirectional obstacle avoidance enhancement (BOAE) mechanism with the deep deterministic policy gradient (DDPG) algorithm, is proposed to enhance the learning ability of obstacle avoidance. Inspired by the analysis of the reaction advantage in dynamic psychology, the BOAE mechanism focuses on obstacle\u2010avoidance reactions from the state and action. The cross\u2010attention mechanism is incorporated to enhance the attention to valuable obstacle\u2010avoidance information. Meanwhile, the obstacle\u2010avoidance behavioral advantage is separately estimated using the modified dueling network. Based on the learning goals of the mobile robot, new assistive reward factors are incorporated into the reward function to promote learning and convergence. The proposed method is validated through several experiments conducted using the simulation platform Gazebo. 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