Resource Management with Deep Reinforcement Learning

By Hongzi Mao, Mohammad Alizadeh, Ishai Menache, Srikanth Kandula. ACM Workshop on Hot Topics in Networks, 2016

1,326 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Networks and Communications, Industrial and Manufacturing Engineering

View Paper: https://www.semanticscholar.org/paper/1ab7aa767e1779c87d822325859e47fe2986e6b2

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Citations in the corpus

Spectrum Sharing in Vehicular Networks Based on Multi-Agent Reinforcement Learning (2019, background): “In particular, recent success of deep RL in humanlevel video game play [7] and AlphaGo [8] has sparked a flurry of interest in applying RL techniques to solve problems from a wide variety of areas and remarkable progress has been made ever since [9], [10], [11].”
LIIR: Learning Individual Intrinsic Reward in Multi-Agent Reinforcement Learning (2019, background): “Many real-world problems, such as traffic light control [1], coordination of autonomous vehicles [2], resources management [3] and multi-player video games [4, 5], can be naturally formulated into cooperative multi-agent systems, where the objective is to maximize the return in the perspective of a ”
Optimizing Federated Learning on Non-IID Data with Reinforcement Learning (2020, background): “The recent success of RL [12, 13] comes from the capability that RL agents learn from the interaction with a dynamic environment.”
Deep Learning Meets SAR: Concepts, models, pitfalls, and perspectives (2020, methodology): “DeepRL uses different types of neural networks to create these functions [41][42].”
Semisupervised Deep Reinforcement Learning in Support of IoT and Smart City Services (2018, background): “[22] formulated the problem of job scheduling with multiple resource demands as a DRL process.”
Learning scheduling algorithms for data processing clusters (2018, methodology): “For example, if the objective is to minimize average JCT, Decima penalizes the agent −τ × J at each time step, where τ is the absolute time (in seconds) since last action and J is the number of jobs in the system [28].”
Learning to Dispatch for Job Shop Scheduling via Deep Reinforcement Learning (2020, background): “In [23], a DRL agent is proposed to learn job scheduling policies for a compute cluster.”
Domain-specific Genetic Algorithm for Multi-tenant DNNAccelerator Scheduling (2021, background): “Some works leverage RL for jobs ordering over clusters such as DeepRM [66], Decima [67] and Thamsen et al. [109].”
Sequence Modeling of Temporal Credit Assignment for Episodic Reinforcement Learning (2019, background): “…the well-known policy gradient algorithms [24, 31, 32] and deep Q-networks [25], have shown superior performance and great potential in many difﬁcult real-world problems, such as the Go game [33, 34], locomotive continuous control problems [21], resource management [23], and robotics [20].”
Neural Adaptive Video Streaming with Pensieve (2017, methodology): “Pensieve uses modern reinforcement learning (RL) techniques [27, 29, 42] to learn a control policy for bitrate adaptation purely through experience.”
Reinforcement Learning for Primary care e Appointment Scheduling (2017, background): “When such heuristics are designed the common approach is the following [1]:”
Machine Learning for Resource Management in Cellular and IoT Networks: Potentials, Current Solutions, and Open Challenges (2019, methodology): “allocation in wireless systems is presented in [78] where the authors used DRL algorithms to manage resources efficiently in the presence of interference and noise.”
Device Placement Optimization with Reinforcement Learning (2017, background): “For example, Mao et al. (2016) pro-pose to train a resource management algorithm with policy gradients.”
Deep Learning for Intelligent Wireless Networks: A Comprehensive Survey (2018, neutral): “[90] proposed to use reinforcement learning in”