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GENERATING AND PROVIDING PROPOSED DIGITAL ACTIONS IN HIGH-DIMENSIONAL ACTION SPACES USING REINFORCEMENT LEARNING MODELS
GENERATING AND PROVIDING PROPOSED DIGITAL ACTIONS IN HIGH-DIMENSIONAL ACTION SPACES USING REINFORCEMENT LEARNING MODELS
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机译:使用强化学习模型在高维动作空间中生成和提供建议的数字动作
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摘要
The present disclosure relates to generating proposed digital actions in high-dimensional action spaces for client devices utilizing reinforcement learning models. For example, the disclosed systems can utilize a supervised machine learning model to train a latent representation decoder to determine proposed digital actions based on latent representations. Additionally, the disclosed systems can utilize a latent representation policy gradient model to train a state-based latent representation generation policy to generate latent representations based on the current state of client devices. Subsequently, the disclosed systems can identify the current state of a client device and a plurality of available actions, utilize the state-based latent representation generation policy to generate a latent representation based on the current state, and utilize the latent representation decoder to determine a proposed digital action from the plurality of available actions by analyzing the latent representation.
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