reinforcement learning

Bridging Ecological Inference and Decision Optimization for Conservation Using Artificial Intelligence

This work developed an adaptive decision model that selects production and distribution decisions of the supplementation program in response to the observed demographic, hydrological, and genetic environment and demonstrates that minimum sub-population size and total population size were primary drivers of the model’s...

Research on supply chain resilience-cost optimization joint decision making in multinational enterprise digital transformation based on multi-task deep reinforcement learning

The results suggest that MT-DRL can improve the simulated resilience-cost trade-off, while further validation with transparent datasets and real-world deployment tests remains necessary.

CloudSafe-TP: Hybrid AI-Based SLO-Aware Predictive Thread-Pool Control for Distributed Cloud Backends

Overall, CloudSafe-TP provides predictive and uncertainty-aware pool-size adjustment that improves P99 SLO protection during bursty workloads while avoiding unnecessary concurrency and resource usage under moderate load.

Do Reinforcement Learning Agents Improve Commodity Sector Rotation? Walk-Forward Evidence from Expert Selection, Strong Benchmarks, and a Frozen-Policy Temporal Extension

This paper tests whether reinforcement learning improves monthly commodity-sector rotation once the experiment is reconstructed from investable instruments, strong active benchmarks, realistic costs, and strictly chronological validation. Total returns for energy (DBE), gold (GLD), agriculture (DBA), and base metals (D...

Causal trust aware federated multi agent reinforcement learning for 6G edge networks

Reinforcement learning-guided archival question answering with adaptive curriculum learning

A reinforcement learning-guided question answering model specifically designed for archival question answering scenarios, incorporating a hierarchical reward mechanism that evaluates answer quality across multiple dimensions including factual accuracy, semantic relevance, and provenance clarity is proposed.

Executable flexibility coordination of electric vehicle aggregators via temporal reinforcement learning in coupled electricity markets

Deep reinforcement learning enhanced coordination of hybrid battery–supercapacitor storage under renewable variability in DC microgrids

Decentralized Topology Robustness Optimization for IoT via Multi-Agent Graph Reinforcement Learning

The robustness of Internet of Things (IoT) communication topologies against internal failures and external perturbations is a fundamental prerequisite for maintaining system stability. This paper studies IoT topology robustness from two perspectives: robustness metric and optimization. Existing robustness metrics, prim...

Reinforcement learning-based distributed adaptive consensus control for stochastic non-affine multi-agent systems subjected to switching topologies and hybrid uncertainties

Spatiotemporal graph-based surrogate modeling and deep reinforcement learning for multi-layer injection-production decision in waterflood reservoirs

Reinforcement learning-based adaptive optimal finite-time control for stochastic nonlinear systems with output hysteresis using identifier-critic-actor framework