Advancements in Reinforcement Learning for Complex Systems

The field of reinforcement learning is witnessing significant advancements in its application to complex systems, including energy trading, supply chain optimization, and network resource allocation. Researchers are developing innovative frameworks that integrate reinforcement learning with other techniques, such as multi-objective optimization, game theory, and deep learning, to tackle challenging problems in these domains. Notably, the use of reinforcement learning is enabling the development of more efficient, adaptive, and fair systems, with applications in areas such as peer-to-peer energy trading, queuing network routing, and service function chain emulation.

Some noteworthy papers in this area include: The Explainable Equity-Aware P2P Energy Trading Framework, which proposes a novel framework for fair and dynamic energy allocation in community microgrids. The Virne benchmarking framework, which provides a comprehensive platform for evaluating deep reinforcement learning-based methods for network resource allocation in NFV. The RoCE BALBOA RDMA-stack, which enables the development of scalable and customizable accelerators and smartNICs for data-intensive applications.

Sources

An Explainable Equity-Aware P2P Energy Trading Framework for Socio-Economically Diverse Microgrid

Simulation-Driven Reinforcement Learning in Queuing Network Routing Optimization

Virne: A Comprehensive Benchmark for Deep RL-based Network Resource Allocation in NFV

Reinforcement Learning for Multi-Objective Multi-Echelon Supply Chain Optimisation

VAE-GAN Based Price Manipulation in Coordinated Local Energy Markets

RoCE BALBOA: Service-enhanced Data Center RDMA for SmartNICs

Deep Reinforcement Learning for Real-Time Green Energy Integration in Data Centers

Deep Reinforcement Learning-based Cell DTX/DRX Configuration for Network Energy Saving

Structure-Informed Deep Reinforcement Learning for Inventory Management

OpenRASE: Service Function Chain Emulation

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