Strategy collaborative selection method based on deep reinforcement learning DDPG algorithm framework

A reinforcement learning and strategy technology, applied in the field of reinforcement learning, can solve problems such as excessive fluctuation of the strategy network, and achieve the effect of increasing generalization, improving overestimation problems, and improving overfitting.
CN112906885APending Publication Date: 2021-06-04UNIV OF ELECTRONICS SCI & TECH OF CHINA
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Patent Information

Authority / Receiving Office
CN Β· China
Patent Type
Applications(China)
Current Assignee / Owner
UNIV OF ELECTRONICS SCI & TECH OF CHINA
Publication Date
2021-06-04

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Abstract

The invention discloses a strategy cooperation selection method based on a deep reinforcement learning DDPG algorithm framework. According to the strategy cooperation selection method, an output action is selected by adopting a strategy cooperation mode, a pair of strategy network output actions are used for evaluation, a Q value obtained by evaluation is used as a weight, and the action is selected by using a probability. Policy cooperation can reduce the possibility of local optimum, improve overfitting, reduce strategy fluctuation and increase stability. And in addition, dropout is added into the actor network, so that the coupling is reduced, the generalization is improved, and the training speed is increased. Meanwhile, according to the thought of a TD3 algorithm, noise is added after the action is selected by the actor target network so as to reduce the size of errors, the network structure of the DDPG is changed, the problem of over-estimation of the DDPG is effectively solved, and the problem of overlarge fluctuation of a strategy network is avoided.
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Description

technical field

[0001] The present invention relates to the technical field of reinforcement learning, in particular to a method for strategic cooperation selection based on a deep reinforcement learning DDPG algorithm framework. Background technique

[0002] The problem discussed in reinforcement learning is how an agent finds a strategy to maximize the reward it can obtain in a complex and uncertain environment. Lillicrap et al. proposed the DDPG (deep deterministic policy gradient) algorithm in 2015, which is a deep reinforcement learning algorithm on the actor-critic framework (Lillicrap, T.P., Hunt, J.J., Pritzel, A., Heess, N., Erez , T., Tassa, Y., Silver, D., & Wierstra, D. (2015). Continuous control with deep reinforcement learning.). DDPG is the first reinforcement learning algorithm to efficiently solve many high-dimensional continuous control tasks. It is also a deterministic policy gradient algorithm based on actor-critic architecture. Contains actor current ...

Claims

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