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When the deep reinforcement learning algorithm based on visual perception is applied to the issue of robot’s dynamic obstacle avoidance, the perception ability of convolutional neural network is combined with the decision control ability of reinforcement learning, and the process from raw visual input to decision action output is realized. But the application scope of deep reinforcement learning algorithm based on Q value is still in low-dimensional and discrete action space. If the continuous action space is discretized, the problem of excessively huge motion space and extremely difficult convergence of network model will be caused. Besides, fine adjustment cannot be realized for the network model, and meanwhile, the division of motion space will also result in information loss. Hence, a deep reinforcement learning algorithm based on deterministic strategy gradient was proposed in this paper, and the strategy was parameterized via convolutional neural network through the integration of reinforcement learning algorithms based on strategy and value.

The Realization of Mobile Robotrs Dynamic Obstacle Avoidance with Deep Reinforcement Learning Based on Deterministic Strategy Gradient