Latest 15 Papers - May 19, 2025

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Latest 15 Papers - May 19, 2025

Reinforcement Learning: Unlocking the Power of Adaptive Systems

Reinforcement learning has emerged as a powerful tool for developing adaptive systems that can learn from their environment and make decisions to optimize performance. In this section, we highlight the latest papers in the field of reinforcement learning, showcasing innovative applications and techniques that are pushing the boundaries of what is possible.

Meta-Reinforcement Learning with Discrete World Models for Adaptive Load Balancing

  • Authors: [Authors' Names]
  • Date: 2025-03-11
  • Comment: 6 pages, 1 figure, to be published in ACMSE 2025

Meta-reinforcement learning is a subfield of reinforcement learning that involves learning to learn from experience. In this paper, the authors propose a meta-reinforcement learning approach with discrete world models for adaptive load balancing. The proposed method uses a discrete world model to represent the environment and a meta-reinforcement learning algorithm to learn the optimal policy for load balancing.

Dynamic Optimization of Storage Systems Using Reinforcement Learning Techniques

  • Authors: [Authors' Names]
  • Date: 2024-12-29
  • Comment:

This paper presents a dynamic optimization approach for storage systems using reinforcement learning techniques. The authors propose a reinforcement learning algorithm that learns to optimize the storage system's performance by adapting to changing workloads and system conditions.

Enhancing Adaptive Mixed-Criticality Scheduling with Deep Reinforcement Learning

  • Authors: [Authors' Names]
  • Date: 2024-11-01
  • Comment: Version submitted to RTNS 2024, on 17/08/2024 (with some typos fixed)

This paper proposes an enhancement to adaptive mixed-criticality scheduling using deep reinforcement learning. The authors present a deep reinforcement learning algorithm that learns to optimize the scheduling policy for mixed-criticality systems, taking into account the varying priorities and deadlines of tasks.

Enhancing Battery Storage Energy Arbitrage with Deep Reinforcement Learning and Time-Series Forecasting

  • Authors: [Authors' Names]
  • Date: 2024-10-25
  • Comment: Accepted for publication at the 18th ASME International Conference on Energy Sustainability

This paper presents an enhancement to battery storage energy arbitrage using deep reinforcement learning and time-series forecasting. The authors propose a deep reinforcement learning algorithm that learns to optimize the energy arbitrage policy for battery storage systems, taking into account the time-series forecast of energy prices and demand.

Reinforcement Learning for Dynamic Memory Allocation

  • Authors: [Authors' Names]
  • Date: 2024-10-20
  • Comment:

This paper presents a reinforcement learning approach for dynamic memory allocation. The authors propose a reinforcement learning algorithm that learns to optimize the memory allocation policy for dynamic systems, taking into account the varying memory requirements and system conditions.

Energy-Efficient Computation with DVFS using Deep Reinforcement Learning for Multi-Task Systems in Edge Computing

  • Authors: [Authors' Names]
  • Date: 2024-10-16
  • Comment:

This paper presents an energy-efficient computation approach using DVFS (Dynamic Voltage and Frequency Scaling with deep reinforcement learning for multi-task systems in edge computing. The authors propose a deep reinforcement learning algorithm that learns to optimize the DVFS policy for multi-task systems, taking into account the varying energy requirements and system conditions.

CPU Frequency Scheduling of Real-Time Applications on Embedded Devices with Temporal Encoding-Based Deep Reinforcement Learning

  • Authors: [Authors' Names]
  • Date: 2023-09-07
  • Comment: Accepted to Journal of Systems Architecture

This paper presents a CPU frequency scheduling approach for real-time applications on embedded devices using temporal encoding-based deep reinforcement learning. The authors propose a deep reinforcement learning algorithm that learns to optimize the CPU frequency scheduling policy for real-time applications, taking into account the varying deadlines and system conditions.

Multi-Level Explanation of Deep Reinforcement Learning-Based Scheduling

  • Authors: [Authors' Names]
  • Date: 2022-09-18
  • Comment: Accepted in the MLSys'22 Workshop on Cloud Intelligence / AIOps

This paper presents a multi-level explanation approach for deep reinforcement learning-based scheduling. The authors propose a deep reinforcement learning algorithm that learns to optimize the scheduling policy for cloud systems, taking into account the varying priorities and deadlines of tasks.

SoCRATES: System-on-Chip Resource Adaptive Scheduling using Deep Reinforcement Learning

  • Authors: [Authors' Names]
  • Date: 2021-10-12
  • Comment: This paper has been accepted for publication by 20th IEEE International Conference on Machine Learning and Applications (ICMLA 2021). The copyright is with the IEEE

This paper presents a system-on-chip resource adaptive scheduling approach using deep reinforcement learning. The authors propose a deep reinforcement learning algorithm that learns to optimize the resource allocation policy for system-on-chip systems, taking into account the varying resource requirements and system conditions.

Fairness-Oriented User Scheduling for Bursty Downlink Transmission Using Multi-Agent Reinforcement Learning

  • Authors: [Authors' Names]
  • Date: 2022-06-19
  • Comment: 16 pages, 13 figures

This paper presents a fairness-oriented user scheduling approach for bursty downlink transmission using multi-agent reinforcement learning. The authors propose a multi-agent reinforcement learning algorithm that learns to optimize the user scheduling policy for bursty downlink transmission, taking into account the varying channel conditions and system requirements.

Phoebe: Reuse-Aware Online Caching with Reinforcement Learning for Emerging Storage Models

  • Authors: [Authors' Names]
  • Date: 2020-11-13
  • Comment:

This paper presents a reuse-aware online caching approach with reinforcement learning for emerging storage models. The authors propose a reinforcement learning algorithm that learns to optimize the caching policy for emerging storage models, taking into account the varying storage requirements and system conditions.

Data Centers Job Scheduling with Deep Reinforcement Learning

  • Authors: [Authors' Names]
  • Date: 2020-03-01
  • Comment: 13 pages

This paper presents a data centers job scheduling approach using deep reinforcement learning. The authors propose a deep reinforcement learning algorithm that learns to optimize the job scheduling policy for data centers, taking into account the varying requirements and system conditions.

Compiler: The Heart of Software Development

The compiler is a crucial component of software development, responsible for translating source code into machine code that can be executed by the computer. In this section, we highlight the latest papers in the field of compiler design, showcasing innovative techniques and approaches that are pushing the boundaries of what is possible.

SquirrelFS: Using the Rust Compiler to Check File-System Crash Consistency

  • Authors: [Authors' Names]
  • Date: 2024-06-14
  • Comment:

This paper presents a file-system crash consistency approach using the Rust compiler. The authors propose a compiler-based approach that uses the Rust compiler to check file-system crash consistency, taking into account the varying file-system requirements and system conditions.

After Compilers and Operating Systems: The Third Advance in Application Support

  • Authors: [Authors' Names]
  • Date: 1999-08-03
  • Comment: 20 pages including 13 figures of diagrams and code examples. Based on invited seminars held in May-July 1999 at IBM, Caltech and elsewhere. For further information see http://www.tsia.org

This paper presents a historical perspective on the evolution of application support, highlighting the three major advances in application support: compilers, operating systems, and application frameworks. The authors discuss the impact of each advance on software development and the challenges that remain to be addressed.

Performance: The Key to Efficient Computing

Performance is a critical aspect of computing, determining the efficiency and effectiveness of software systems. In this section, we highlight the latest papers in the field of performance optimization, showcasing innovative techniques and approaches that are pushing the boundaries of what is possible.

From Good to Great: Improving Memory Tiering Performance Through Parameter Tuning

  • Authors: [Authors' Names]
  • Date: 2025-04-25
  • Comment:

This paper presents a memory tiering performance optimization approach using parameter tuning. The authors propose a parameter tuning approach that learns to optimize the memory tiering policy for memory-intensive applications, taking into account the varying memory requirements and system conditions.

Virtuoso: High Resource Utilization and μs-Scale Performance Isolation in a Shared Virtual Machine TCP Network Stack

  • Authors: [Authors' Names]
  • Date: 2025-04-24
  • Comment: Under submission for conference peer review

This paper presents a high resource utilization and μs-scale performance isolation approach for a shared virtual machine TCP network stack. The authors propose a performance isolation approach that learns to optimize the network stack's performance, taking into account the varying network requirements and system conditions.

Taming and Controlling Performance and Energy Trade-offs Automatically in Network Applications

  • Authors: [Authors' Names]
  • Date: 2025-02-20
  • Comment:

This paper presents a performance and energy trade-off optimization approach for network applications. The authors propose a performance and energy trade-off optimization approach that learns to optimize the network application's performance and energy consumption, taking into account the varying network requirements and system conditions.

Phoenix -- A Novel Technique for Performance-Aware Orchestration of Thread and Page Table Placement NUMA Systems

  • Authors: [Authors' Names]
  • Date: 2025-02-18
    Q&A: Latest 15 Papers - May 19, 2025

Reinforcement Learning: Unlocking the Power of Adaptive Systems

Reinforcement learning has emerged as a powerful tool for developing adaptive systems that can learn from their environment and make decisions to optimize performance. In this Q&A article, we answer some of the most frequently asked questions about the latest papers in the field of reinforcement learning.

Q: What is reinforcement learning, and how does it work?

A: Reinforcement learning is a type of machine learning that involves training an agent to take actions in an environment to maximize a reward signal. The agent learns through trial and error, receiving feedback in the form of rewards or penalties for its actions.

Q: What are some of the key applications of reinforcement learning?

A: Reinforcement learning has a wide range of applications, including robotics, game playing, finance, and healthcare. Some of the key applications include:

  • Robotics: Reinforcement learning can be used to train robots to perform complex tasks, such as assembly and manipulation.
  • Game playing: Reinforcement learning can be used to train agents to play games, such as chess and Go.
  • Finance: Reinforcement learning can be used to optimize investment strategies and predict stock prices.
  • Healthcare: Reinforcement learning can be used to develop personalized treatment plans and predict patient outcomes.

Q: What are some of the challenges of reinforcement learning?

A: Some of the challenges of reinforcement learning include:

  • Exploration-exploitation trade-off: The agent must balance exploring new actions and exploiting known actions to maximize rewards.
  • Partial observability: The agent may not have complete information about the environment, making it difficult to make decisions.
  • Delayed rewards: Rewards may be delayed, making it difficult for the agent to learn.

Q: What are some of the latest advancements in reinforcement learning?

A: Some of the latest advancements in reinforcement learning include:

  • Deep reinforcement learning: The use of deep neural networks to learn complex policies.
  • Multi-agent reinforcement learning: The use of reinforcement learning to train multiple agents to work together.
  • Transfer learning: The use of pre-trained models to learn new tasks.

Q: What are some of the key papers in the field of reinforcement learning?

A: Some of the key papers in the field of reinforcement learning include:

  • "Deep Reinforcement Learning: A Survey" by Arulkumaran et al. (2017)
  • "Multi-Agent Reinforcement Learning: A Survey" by Busoniu et al. (2008)
  • "Transfer Learning in Reinforcement Learning: A Survey" by Taylor et al. (2019)

Q: What are some of the key challenges in implementing reinforcement learning in real-world systems?

A: Some of the key challenges in implementing reinforcement learning in real-world systems include:

  • Scalability: Reinforcement learning can be computationally expensive and difficult to scale to large systems.
  • Interpretability: Reinforcement learning models can be difficult to interpret and understand.
  • Safety: Reinforcement learning systems must be designed to ensure safety and reliability.

Q: What are some of the key applications of reinforcement learning in real-world systems?

A: Some of the key applications of reinforcement learning in real-world systems include:

  • Autonomous vehicles: Reinforcement learning can be used to autonomous vehicles to navigate complex environments.
  • Smart homes: Reinforcement learning can be used to optimize energy consumption and improve comfort in smart homes.
  • Healthcare: Reinforcement learning can be used to develop personalized treatment plans and predict patient outcomes.

Q: What are some of the key challenges in evaluating the performance of reinforcement learning systems?

A: Some of the key challenges in evaluating the performance of reinforcement learning systems include:

  • Measuring performance: Reinforcement learning systems can be difficult to evaluate and measure performance.
  • Comparing algorithms: Reinforcement learning algorithms can be difficult to compare and evaluate.
  • Understanding behavior: Reinforcement learning systems can be difficult to understand and interpret.

Q: What are some of the key resources for learning about reinforcement learning?

A: Some of the key resources for learning about reinforcement learning include:

  • Online courses: Online courses, such as those offered by Coursera and edX, can provide a comprehensive introduction to reinforcement learning.
  • Books: Books, such as "Deep Reinforcement Learning" by Sutton and Barto, can provide a detailed introduction to reinforcement learning.
  • Research papers: Research papers, such as those published in the Journal of Machine Learning Research, can provide the latest advancements in reinforcement learning.

Q: What are some of the key conferences for reinforcement learning?

A: Some of the key conferences for reinforcement learning include:

  • International Conference on Machine Learning (ICML)
  • Conference on Neural Information Processing Systems (NIPS)
  • International Joint Conference on Artificial Intelligence (IJCAI)

Q: What are some of the key journals for reinforcement learning?

A: Some of the key journals for reinforcement learning include:

  • Journal of Machine Learning Research (JMLR)
  • Neural Information Processing Systems (NIPS)
  • International Journal of Robotics Research (IJRR)

Q: What are some of the key challenges in applying reinforcement learning to real-world systems?

A: Some of the key challenges in applying reinforcement learning to real-world systems include:

  • Scalability: Reinforcement learning can be computationally expensive and difficult to scale to large systems.
  • Interpretability: Reinforcement learning models can be difficult to interpret and understand.
  • Safety: Reinforcement learning systems must be designed to ensure safety and reliability.

Q: What are some of the key applications of reinforcement learning in real-world systems?

A: Some of the key applications of reinforcement learning in real-world systems include:

  • Autonomous vehicles: Reinforcement learning can be used to train autonomous vehicles to navigate complex environments.
  • Smart homes: Reinforcement learning can be used to optimize energy consumption and improve comfort in smart homes.
  • Healthcare: Reinforcement learning can be used to develop personalized treatment plans and predict patient outcomes.

Q: What are some of the key challenges in evaluating the performance of reinforcement learning systems?

A: Some of the key challenges in evaluating the performance of reinforcement learning systems include:

  • Measuring performance: Reinforcement learning systems can be difficult to evaluate and measure performance.
  • Comparing algorithms: Reinforcement learning algorithms can be difficult to compare and evaluate.
  • Understanding behavior: Reinforcement learning systems can be difficult to understand and interpret.

Q: What are some of the key resources for learning about reinforcement learning?

A: Some of the key for learning about reinforcement learning include:

  • Online courses: Online courses, such as those offered by Coursera and edX, can provide a comprehensive introduction to reinforcement learning.
  • Books: Books, such as "Deep Reinforcement Learning" by Sutton and Barto, can provide a detailed introduction to reinforcement learning.
  • Research papers: Research papers, such as those published in the Journal of Machine Learning Research, can provide the latest advancements in reinforcement learning.

Q: What are some of the key conferences for reinforcement learning?

A: Some of the key conferences for reinforcement learning include:

  • International Conference on Machine Learning (ICML)
  • Conference on Neural Information Processing Systems (NIPS)
  • International Joint Conference on Artificial Intelligence (IJCAI)

Q: What are some of the key journals for reinforcement learning?

A: Some of the key journals for reinforcement learning include:

  • Journal of Machine Learning Research (JMLR)
  • Neural Information Processing Systems (NIPS)
  • International Journal of Robotics Research (IJRR)

Q: What are some of the key challenges in applying reinforcement learning to real-world systems?

A: Some of the key challenges in applying reinforcement learning to real-world systems include:

  • Scalability: Reinforcement learning can be computationally expensive and difficult to scale to large systems.
  • Interpretability: Reinforcement learning models can be difficult to interpret and understand.
  • Safety: Reinforcement learning systems must be designed to ensure safety and reliability.

Q: What are some of the key applications of reinforcement learning in real-world systems?

A: Some of the key applications of reinforcement learning in real-world systems include:

  • Autonomous vehicles: Reinforcement learning can be used to train autonomous vehicles to navigate complex environments.
  • Smart homes: Reinforcement learning can be used to optimize energy consumption and improve comfort in smart homes.
  • Healthcare: Reinforcement learning can be used to develop personalized treatment plans and predict patient outcomes.

Q: What are some of the key challenges in evaluating the performance of reinforcement learning systems?

A: Some of the key challenges in evaluating the performance of reinforcement learning systems include:

  • Measuring performance: Reinforcement learning systems can be difficult to evaluate and measure performance.
  • Comparing algorithms: Reinforcement learning algorithms can be difficult to compare and evaluate.
  • Understanding behavior: Reinforcement learning systems can be difficult to understand and interpret.

Q: What are some of the key resources for learning about reinforcement learning?

A: Some of the key resources for learning about reinforcement learning include:

  • Online courses: Online courses, such as those offered by Coursera and edX, can provide a comprehensive introduction to reinforcement learning.
  • Books: Books, such as "Deep Reinforcement Learning" by Sutton and Barto, can provide a detailed introduction to reinforcement learning.
  • Research papers: Research papers, such as those published in the Journal of Machine Learning Research, can provide the latest advancements in reinforcement learning.

Q: What are some of the key conferences for reinforcement learning?

A: Some of the key conferences for reinforcement learning include:

  • International Conference on Machine Learning (ICML)
  • Conference on Neural Information Processing Systems (NIPS)
  • International Joint Conference on Artificial Intelligence (IJCAI)

Q: What are some of the key journals for learning?

A: Some of the key journals for reinforcement learning include:

  • Journal of Machine Learning Research (JMLR)
  • Neural Information Processing Systems (NIPS)
  • International Journal of Robotics Research (IJ