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reinforcement learning

2,493 papers

#reinforcement learning Open access Oct 2026

A robust model for improving digital marketing using sentiment analysis and two-agent off-policy proximal policy optimization

Understanding consumer sentiment is important in digital marketing because it strongly influences brand perception and purchasing decisions. This paper proposes a novel two-agent off-policy proximal policy optimization (PPO) framework. Unlike standard multi-agent reinforcement learning (RL) approaches, the proposed fra...

Mohammad Yarjanli, Neda Mahdinasab · 0 citations
#reinforcement learning Open access Oct 2026

Effectiveness of experiential practice on environmental awareness and pro-environmental behavior: evidence from junior high school students in Toride City, Japan

Abstract This study verified the effects of “experiential practice” and “visualizing” carbon dioxide reduction on the transformation of junior high school students' environmental awareness and pro-environmental behavior (PEB). We sought to propose measures for sustaining high environmental awareness and choice of appro...

Shingo Fujii, Kiyokazu Ujiie, Takeshi Mizunoya · 0 citations
#reinforcement learning Open access Oct 2026

Reproducibility companion for Physics-Guided Task Formulation as a Trainability Prior for Reinforcement Learning in Autonomous Motion Planning

Audited data and code archive for the associated autonomous-motion-planning manuscript. Includes 65 raw training-stat MAT files, 47 executed 100-scenario final-test MAT files, 47 selected Stage-10 checkpoints, frozen final and validation scenario banks, run-level ledgers, and verification scripts. Independent regenerat...

Yusuf Bilfaqih, Ari Santoso, Joko Susila et al. · 0 citations
#reinforcement learning Dataset Open access Oct 2026

ReactFlow: Pretrained Checkpoints for Transition State and Reaction Pathway Generation

ABOUT ReactFlow is a flow-matching model that generates the transition state (TS) and the surrounding reaction pathway from reactant and product structures. It uses an SO(3)-equivariant EquiformerV2 backbone (35.3M parameters) and is trained in two stages: flow-matching pretraining, followed by GRPO reinforcement-learn...

Yifang Qin · 0 citations
#reinforcement learning Open access Oct 2026

Security-Aware Adaptive Computation Offloading in Mobile Edge Computing Using Reinforcement Learning and Deep Q-Networks

Mobile Edge Computing (MEC) enables resource-constrained mobile devices to offload computation-intensive tasks to nearby edge servers. Existing computation offloading approaches primarily optimise latency, energy consumption, or resource allocation, but often do not consider security constraints and multi-user queue st...

Brindeshwar Sharma · 0 citations
#reinforcement learning Open access Oct 2026

Quantum Electronics Explained: Quantum Systems, Devices & Advanced Technologies

Quantum Electronics Explained: Quantum Systems, Devices & Advanced Technologies is a publication-grade Open Educational Resource (OER) module covering the physical principles, macroscopic quantum mechanics, circuit quantum electrodynamics (cQED), and cryogenic microwave control governing quantum hardware. Serving as an...

Prep4Uni.Online · 0 citations
#reinforcement learning Dataset Open access Oct 2026

ReactFlow: Pretrained Checkpoints for Transition State and Reaction Pathway Generation

ABOUT ReactFlow is a flow-matching model that generates the transition state (TS) and the surrounding reaction pathway from reactant and product structures. It uses an SO(3)-equivariant EquiformerV2 backbone (35.3M parameters) and is trained in two stages: flow-matching pretraining, followed by GRPO reinforcement-learn...

Yifang Qin · 0 citations
#reinforcement learning Open access Oct 2026

Radial-residual energy-aware deep learning framework for sustainable cloud–IoT intelligence in smart cities

Smart city Internet of Things (IoT) networks is generating a continuous stream of heterogeneous sensor data that tends to require a timely analysis under the strict energy, latency, and computational constraints. Existing cloud-edge learning approaches have improved IoT intelligence, but they often treat feature learni...

Kathiresan Jayabalan, P. Sreelatha, T. Dakshinamurthy et al. · 0 citations
#reinforcement learning Open access Oct 2026

A fault-aware, explainable proximal policy optimization based intelligent control framework with neuromorphic-inspired encoding for voltage and frequency stability

Abstract High penetration of inverter-interfaced generation, faults, and sudden load changes in power systems makes them more vulnerable to voltage and frequency deviations. Traditional controllers often struggle to adapt to varying disturbance types and severities in real time. Adaptive, data-driven control methods ar...

Niharika Agrawal, Sheila Mahapatra, Neeraj Kanwar et al. · 0 citations
#reinforcement learning Dataset Open access Oct 2026

ProphetRouterRL: code, simulation setup and results

Code, simulation setup and results accompanying the paper "ProphetRouterRL: A Reinforcement Learning Enhanced Routing Protocol for V2X Delay Tolerant Networks" (submitted to MDPI Network). ProphetRouterRL keeps PRoPHET's delivery-predictability comparison as a first filter and adds a small tabular Q-learning agent that...

Usama Shaker Hachim, Asma Abu-Samah, El Arbi Abdellaoui Alaoui et al. · 0 citations
#reinforcement learning Open access Oct 2026

Risk-aware hierarchical reinforcement learning for investment and operations in cross-border energy supply chains under the belt and road initiative

Cross-border energy supply chains under the Belt and Road Initiative (BRI) face unprecedented challenges, including geopolitical instability, macroeconomic volatility, and operational disruptions. Traditional optimization or flat reinforcement learning (RL) methods often struggle to address the scale, uncertainty, an...

Jia-Long Mi · 0 citations

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MIT News · Artificial Intelligence Oct 23, 2026

Q&A: How AI came to model the “messy” human mind

Assistant Professor Benjamin Lindquist studies how engineers deliberately injected randomness into early AI, giving rise to deep-learning models that sometimes hallucinate.

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