Depth reinforcement learning strategy optimization defense method and device based on imitation learning

Abstract

The invention discloses a deep reinforcement learning strategy optimization defense method and device based on imitation learning, and the method comprises the steps: building an agent automatic driving simulation environment of deep reinforcement learning, constructing a target agent based on a deep Q network in reinforcement learning, and carrying out the reinforcement learning of the target agent to optimize the parameters of the deep Q network; utilizing the parameter-optimized deep Q network to generate a state action pair sequence of the target agent at T moments as expert data, wherein an action value in a state action pair corresponds to an action with a minimum Q value; constructing an adversarial agent based on the generative adversarial network, and performing imitation learning on the adversarial agent, that is, taking the state in the expert data as the input of the generative adversarial network, and taking the expert data as a label to supervise and optimize the parameters of the generative adversarial network; and performing adversarial training on the target agent based on the state generated by the adversarial agent, and then optimizing parameters of the deep Q network to achieve deep reinforcement learning strategy optimization defense.

Description

technical field [0001] The invention belongs to the field of defense oriented to deep reinforcement learning, and in particular relates to a defense method and device based on imitation learning-based deep reinforcement learning strategy optimization. Background technique [0002] Deep reinforcement learning is one of the directions of artificial intelligence that has attracted much attention in recent years. With the rapid development and application of reinforcement learning, reinforcement learning has been widely used in robot control, game gaming, computer vision, unmanned driving and other fields. In order to ensure the safe application of deep reinforcement learning in safety-critical fields, the key is to analyze and discover loopholes in deep reinforcement learning algorithms and models to prevent people with ulterior motives from using these loopholes to conduct illegal profit-making activities. Different from the single-step prediction task of traditional machine l...

AI Technical Summary

Problems solved by technology

[0005] In order to solve the problem that existing reinforcement learning is attacked in the field of safety decision-making (such as automatic driving scenarios) or automatic decision-making leaks, which in turn leads to inacc

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