reinforcement learning

Robust indoor automated guided vehicle navigation for logistics applications via imitation-augmented hierarchical reinforcement learning with a memory-attention generalized network

Comportamientos exitosos del trabajo en suelo durante el campeonato venezolano de Judo 2025, primera categoría: Estudio Observacional

The aim of this investigation was to determine sucessfull behaviors asociated to grounwork from the participants. Under a non-experimental design, 51 actions from 1477 performed attacks were evaluated by: Submission form (inmobilization, strangled, articular luxation); precedent maneuvers, opponent initial position, pe...

Cooperative control of multi-microgrids under uncertainty: a multi-agent actor-critic approach with LSTM and deep dense architecture

The high penetration of renewable energy poses significant source-load uncertainty challenges to the economic and stable operation of microgrids. To address the key challenges of multi-microgrid cooperative optimization, including temporal source-load uncertainty, heterogeneous inter-microgrid coordination, and real-ti...

Optimization of dynamic admission control for preemptible cloud services based on TPD-SAC reinforcement learning

Abstract Preemptible cloud services utilize idle resources from contract-based services. The resources allocated to these low-priority services can be preempted by contract-based services during periods of overload, so cloud platforms offer low prices to attract cost-sensitive users. While preemptible cloud services en...

Social information quality and environmental volatility shape collective foraging behaviour

Abstract Collective foraging is widespread across the animal kingdom, allowing animals to more effectively discover resources. However, collective foragers need to balance a key trade-off between private exploration and using social information. Social information can come in very distinct forms, ranging from simple po...

AI-Driven Autonomous Test Automation Frameworks for Cloud Native Applications: Improving Software Reliability and Continuous Delivery

This paper describes a framework that brings together three capabilities: automatic generation of new test cases from API specifications and usage data, self-healing of broken test scripts through similarity-based and learned matching, and risk-based prioritisation of test execution using a reinforcement learning agent...

AI-Based Optimization for Biofuel Production: Strategies for Utilizing Degraded Land for Climate Change Mitigation, Green Finance Mobilization, and Achieving United Nations Sustainable Development Goals

Global land degradation affects approximately 2 billion hectares, threatening food security, biodiversity, and climate stability while undermining the United Nations Sustainable Development Goals (SDGs). The concurrent urgency to decarbonize the energy system and mobilize green finance for sustainable transitions has c...

Learning from Unreachable Rewards: Hint-Conditioned Reinforcement Learning for Generative Recommendation

This work proposes Hint-Conditioned Generative Recommendation (HCGRec), a semantic-ID generative recommendation framework that recovers learning signal for such hard training instances and introduces hint-aware credit decomposition, using supervised learning to preserve item-semantic and prefix-structure alignment for...

Learning from What You Retrieve: Online RL Fine-Tuning for Semantic Retrieval

This work proposes PAO (Positive-Advantage-Only), a selective RL optimization method that selectively applies gradient updates only to retrieved items with positive advantages, effectively pulling query embed- dings toward high-reward regions while preserving global topo- logical stability.

MedAgent-R1: Faithfulness-Aware Reinforcement Learning for Evidence-Grounded Medical Reasoning

This work identifies a systematic failure mode in RL-trained retrieval agents: outcome-only rewards improve accuracy while degrading faithfulness, a phenomenon the authors term confident hallucination, and addresses this with a faithfulness-gated reward design.

RecRec: Latent Interests Recursive Reasoning for Sequential Recommendation

Rec is proposed, an RL-free framework that decouples reasoning from prediction, overcoming the fixed d-dimensional state bottleneck of prior methods, and points to a decoupled, multi-vector recipe that unleashes latent reasoning from the single-state bottleneck of prior methods.

Quantum gated recurrent neural networks for continuous dynamic temporal decision-making

A novel policy network, quantum gated recurrent neural network (QGRNN), which incorporates quantum superposition encoding and cyclic entanglement structures to achieve more efficient state-space exploration and employs a quantum–classical hybrid residual design that effectively integrates quantum measurement outcomes w...