reinforcement learning

Design of an Adaptive Digital Intervention System for Preventing and Controlling Social Media Addiction in Adolescents: A Personalized Strategy Optimization Method Integrating NLP Sentiment Analysis and Reinforcement Learning

Stepwise Composition of Heuristic Components for Offline Strip Packing: A Controlled Study of Batch Reinforcement Learning

Joint resource allocation and adaptive task scheduling in 5G networks using hybrid deep reinforcement with revolution crisscross and quantum supercell optimization

CHARLES: Continuous Homeostatic Adversarial Resilience through Learning and Epistemic Sifting

The increasing use of artificial intelligence (AI) and reinforcement learning (RL) in cybersecurity introduces the possibility of autonomous cyber agents capable of observing an environment, learning from interaction, and selecting sequential actions with limited human intervention.This development creates a challenge...

Bridging deterministic and stochastic scheduling: Learning risk-aware forecast adjustments for PV-BESS operation

The participation of hybrid photovoltaic (PV) and battery energy storage system (BESS) installations in electricity markets requires modeling multi-temporal forecast uncertainty via temporal trajectories and integrating this information into bidding optimization frameworks. Most state-of-the-art approaches rely on stoc...

AI Alignment Without Representation: From Scalar Rewards to Social Deliberation

Despite the widespread adoption of Reinforcement Learning from Human Feedback, state-of-the-art AI systems remain prone to two persistent failures. The first is hallucination, or the production of fluent but false content. The second is moral drift, or the convergence toward exploitative or harmful equilibria under sus...

Explainable machine learning for shear capacity prediction of reinforced concrete deep beams

The shear capacity prediction of reinforced concrete (RC) deep beams remains a challenging issue in design of concrete structures. Although the code-based strut-and-tie method is widely accepted, it is often time-consuming for routine design applications, while existing empirical equations are unable to fully capture t...

A hierarchical deep reinforcement learning-model predictive control framework for lithium-ion battery fast charging under expansion-force constraints

Deep reinforcement learning for semi-active structural control using a variable inerter device

Resilient Embodied AI for Smart Farming

Pest infestations remain a hazard for crop yields around the world, frequently going undetected until after irreversible damage has occurred. Existing pest detection systems and processes typically use fixed position sensors and/or human scouting and are slow to deploy, slow to respond, and poorly fit for dynamic envir...

Context shapes action understanding in social interactions

In social interactions, individuals anticipate what others are about to do next, enabling smooth social interactions and coexistence in daily life. Here, we examined how kinematic and contextual cues are weighted and integrated to predict an interactor's actions during dynamic, real-time competitive interactions. We an...

Prior preferences interfere with the associative learning of food values

Addressing unhealthy behaviors requires learning and implementing healthier options. Ecologically, we rarely undergo associative learning in naive environments; our prior preferences confound the learning process. This is perhaps especially true for food, for which we have strong, diverse preferences that may undermine...