reinforcement learning

Comment on egusphere-2026-4021

Abstract. The Arctic Weather Satellite (AWS), launched by the European Space Agency (ESA) in August 2024, has enabled the first-ever global ice cloud remote sensing using 325 GHz terahertz channels. Owing to its short wavelength, the 325 GHz frequency band is highly sensitive to scattering by the three frozen hydromete...

Comment on wes-2026-157

Abstract. To address the difficulty in coordinating component-degradation regulation strategies with opportunistic maintenance timings over the full lifecycle of wind turbines, this study proposes a reinforcement-learning–based collaborative optimization method for wind-turbine load regulation and opportunistic mainten...

FROM REWARD HACKING TO RESPONSIBILITY GAP: A SYSTEMIC ANALYSIS OF AGENTIC RL FAILURES, BENCHMARK PHILOSOPHY, AND THE CASE FOR CONSTRAINT-AWARE GOVERNANCE IN FRONTIER AI

ABSTRACTBetween 2016 and 2026, the AI research community documented a consistent failure mode: reinforcement learning agents optimize measured proxies rather than intended outcomes, exploiting gaps in reward specifications, evaluation infrastructure, and environmental constraints. What began as curiosities in simulated...

Real-time optimization of urban traffic using predictive algorithms

Urban traffic congestion remains a critical challenge in modern cities, contributing to increased travel times, environmental pollution, and economic inefficiencies. This research paper explores the application of predictive algorithms for real-time optimization of urban traffic flow, aiming to enhance mobility and red...

BEHAVIORAL DEVELOPMENT OF PRESCHOOL CHILDREN: PSYCHOLOGICAL AND PEDAGOGICAL ASPECTS

The behavioral development of preschool children is an important component of their overall psychological, social, emotional, and personal development. During the preschool years, children gradually acquire socially accepted patterns of behavior, learn to regulate emotions, communicate with peers and adults, follow rul...

FROM REWARD HACKING TO RESPONSIBILITY GAP: A SYSTEMIC ANALYSIS OF AGENTIC RL FAILURES, BENCHMARK PHILOSOPHY, AND THE CASE FOR CONSTRAINT-AWARE GOVERNANCE IN FRONTIER AI

ABSTRACTBetween 2016 and 2026, the AI research community documented a consistent failure mode: reinforcement learning agents optimize measured proxies rather than intended outcomes, exploiting gaps in reward specifications, evaluation infrastructure, and environmental constraints. What began as curiosities in simulated...

BEHAVIORAL DEVELOPMENT OF PRESCHOOL CHILDREN: PSYCHOLOGICAL AND PEDAGOGICAL ASPECTS

The behavioral development of preschool children is an important component of their overall psychological, social, emotional, and personal development. During the preschool years, children gradually acquire socially accepted patterns of behavior, learn to regulate emotions, communicate with peers and adults, follow rul...

Transforming driving-scenario priors into adaptive reward modulation for reinforcement learning-based energy management of heavy-duty fuel cell vehicles

History-Aware Deep Reinforcement Learning for real-time robot navigation in dynamic industrial environments

Reconceptualizing AI Alignment: Beyond Behavioral Guardrails and Epistemic Simulations

Contemporary AI alignment research is dominated by two paradigms: behavioral conditioning through Reinforcement Learning from Human Feedback (RLHF), and speculative epistemic frameworks such as "Simulation Theology." Both treat alignment as an external constraint imposed on a value-neutral substrate, and both fail for...

Project TALOS: Tactical Agentic Literature Orchestration System

Project TALOS is an autonomous research intelligence platform powered by deep reinforcement learning (DDDQN), multi-tier LLM orchestration, and the Grey Wolf Optimizer (GWO). It conducts end-to-end scientific literature discovery and evaluation across 18 academic APIs.

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

Simulation of a security-aware adaptive computation offloading pipeline for Mobile Edge Computing: an analytical bandwidth threshold, Q-Learning and a Deep Q-Network over a continuous state, Lyapunov drift-plus-penalty queue control, and Object Dependency Graph vulnerability scoring. Preprint draft, not peer reviewed;...