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An improved scheduling with advantage actor-critic for Storm workloads
Various resources as the essential elements of data centers, and their utilization is vital to resource managers. In terms of the persistence, the...
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A World Model for Actor–Critic in Reinforcement Learning
Abstract—Model-based reinforcement learning is a hybrid approach that combines planning with a world model and model-free policy learning, a major...
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A double Actor-Critic learning system embedding improved Monte Carlo tree search
As the bias between the estimated value and the true value, overestimation is a basic problem in reinforcement learning, which leads to a lower total...
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Large-scale UAV swarm confrontation based on hierarchical attention actor-critic algorithm
In large-scale unmanned aerial vehicle (UAV) swarm confrontation scenarios, the design of decision-making and coordination strategies becomes...
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Sampling-efficient path planning and improved actor-critic-based obstacle avoidance for autonomous robots
Autonomous robots have garnered extensive utilization in diverse fields. Among the critical concerns for autonomous systems, path planning holds...
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On the sample complexity of actor-critic method for reinforcement learning with function approximation
Reinforcement learning, mathematically described by Markov Decision Problems, may be approached either through dynamic programming or policy search....
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Image captioning with residual swin transformer and Actor-Critic
Image captioning is one essential work in the multi-modal area, which employs computer vision and natural language processing technology together to...
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UAV-enabled fair offloading for MEC networks: a DRL approach based on actor-critic parallel architecture
Data processing is a key challenge for computationally limited Ground Users (GUs) in various applications. Unmanned Aerial Vehicles (UAVs) equipped...
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Robustness Assessment of Asynchronous Advantage Actor-Critic Based on Dynamic Skewness and Sparseness Computation: A Parallel Computing View
Reinforcement learning as autonomous learning is greatly driving artificial intelligence (AI) development to practical applications. Having...
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Improving actor-critic structure by relatively optimal historical information for discrete system
Recently, actor-critic structure based neural networks are widely used in many reinforcement learning tasks. It consists of two main parts: (i) an...
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Actor-critic reinforcement learning leads decision-making in energy systems optimization—steam injection optimization
Steam injection is a popular technique to enhance oil recovery in mature oil fields. However, the conventional approach of using a constant steam...
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A heuristic multi-objective task scheduling framework for container-based clouds via actor-critic reinforcement learning
Container-based cloud technology has changed the delivery mode of traditional applications and brought a breakthrough development to the field of...
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Optimal fractional-order PID controller based on fractional-order actor-critic algorithm
In this paper, an online optimization approach of a fractional-order PID controller based on a fractional-order actor-critic algorithm (FOPID-FOAC)...
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Integrating short-term stochastic production planning updating with mining fleet management in industrial mining complexes: an actor-critic reinforcement learning approach
Short-term production planning in industrial mining complexes involves defining daily, weekly or monthly decisions that aim to achieve production...
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An Advantage Actor-Critic Deep Reinforcement Learning Method for Power Management in HPC Systems
A primary concern when deploying a High-Performance Computing (HPC) system is its high energy consumption. Typical HPC systems consist of hundreds to...
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Actor-critic multi-objective reinforcement learning for non-linear utility functions
We propose a novel multi-objective reinforcement learning algorithm that successfully learns the optimal policy even for non-linear utility...
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Improved gradient boosting hybrid spectrum sharing and actor critic channel allocation in 6G CR-IOT
The fast advancement of wireless communication technology and the growth in the reputation of Internet of Things (IoT) applications have led to the...
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A novel semi-supervised generative adversarial network based on the actor-critic algorithm for compound fault recognition
Vibration signals can be used to extract effective fault features for fault diagnosis. However, traditional supervised learning requires considerable...
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SAC-FACT: Soft Actor-Critic Reinforcement Learning for Counterfactual Explanations
Explainable AI (XAI) techniques are essential for improving the interpretability of machine learning models, which are generally regarded as black...
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Multi-source Domain Adaptation Based on Data Selector with Soft Actor-Critic
Multi-source domain adaptation (MDA) aims to transfer the knowledge learned from multiple-sources domains to the target domain. Although the source...