Feature filtering defense method for deep reinforcement learning model

A reinforcement learning and model technology, applied in the field of deep learning, can solve problems such as non-normalization, increased training time, and inability to converge, and achieve the effect of improving training efficiency

Active Publication Date: 2020-08-28
ZHEJIANG UNIV OF TECH
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AI Technical Summary

Problems solved by technology

The existence of singular sample data will increase the training time and may also lead to failure to converge. Therefore, when there is singula...

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  • Feature filtering defense method for deep reinforcement learning model
  • Feature filtering defense method for deep reinforcement learning model
  • Feature filtering defense method for deep reinforcement learning model

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Embodiment Construction

[0038] In order to make the object, technical solution and advantages of the present invention clearer, the present invention will be further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described here are only used to explain the present invention, and do not limit the protection scope of the present invention.

[0039] The following embodiments take the game environment as an example, and the agent establishes a relationship with the state of the environment in the interactive environment. The object of defense is the deep reinforcement learning model, and reinforcement learning generally uses Markov Decision Process (MDP) as a formalization method. In an interactive environment, collect the environment to observe the state s and let the agent take action a, and give the reward value R in time according to the change of the environment s, and save the current state, action, rewa...

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Abstract

The invention discloses a feature filtering defense method for a deep reinforcement learning model. The feature filtering defense method comprises the following steps that: (1) aiming at the]= DDPG model for generating continuous behaviors, a system comprises an actor network and a critic network, wherein the actor network comprises an action estimation network and an action implementation network, the critic network comprises a state estimation network and a state implementation network, pre-training is conducted on the deep reinforcement learning model DDPG, and a current state, a behavior,a reward value and a next state of a pre-training stage are stored in a cache region; (2) an auto-encoder is trained, feature filtering is performed on an input state by using an encoder of the trained auto-encoder to obtain a feature map corresponding to the filtered input state, and a feature map is stored in the cache region; and (3) a convolution kernel in the pre-trained DDPG model is pruned,action prediction is performed by using the pruned DPG model, and a prediction action is output and executed.

Description

technical field [0001] The invention belongs to the technical field of deep learning, and in particular relates to a feature filtering defense method for deep reinforcement learning models. Background technique [0002] With the rapid development of artificial intelligence technology, more and more fields have begun to use AI technology. Since the concept of "artificial intelligence" was first proposed in 1956, AI has attracted more and more attention. His research areas include knowledge representation, machine perception, machine thinking, machine learning, and machine behavior, and he has made some achievements in various fields. Reinforcement learning is also a multi-disciplinary product, which itself is a decision science, so it can be found in many branches of disciplines. Reinforcement learning has a wide range of applications, such as: helicopter aerobatics, game AI, investment management, power station control, making robots imitate human walking, etc. [0003] I...

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Application Information

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IPC IPC(8): H04L29/06H04L29/08H04L12/24G06N3/08G06N3/04G06K9/62
CPCH04L63/0227H04L63/1441G06N3/08H04L41/145H04L67/568G06N3/045G06F18/2113G06F18/214
Inventor 陈晋音王雪柯章燕王珏
Owner ZHEJIANG UNIV OF TECH
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