Temporal Consistency-Based Loss Function for Both Deep Q-Networks and Deep Deterministic Policy Gradients for Continuous Actions

Temporal Consistency-Based Loss Function for Both Deep Q-Networks and Deep Deterministic Policy Gradients for Continuous Actions

Artificial intelligence (AI) techniques in power grid control and energy management in building automation require both deep Q-networks (DQNs) and deep deterministic policy gradients (DDPGs) in deep reinforcement learning (DRL) as off-policy algorithms. Most studies on improving the stability of DRL...

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Bibliographic Details
Main Author: Chayoung Kim
Format: Article
Language:English
Published: MDPI AG 2021-12-01
Series:Symmetry
Subjects:
deep Q-network
deep deterministic policy gradient
temporal consistency loss
temporal difference
deep reinforcement learning
target network update
Online Access:https://www.mdpi.com/2073-8994/13/12/2411

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