reinforcement learning

Implementing the Teaching Personal and Social Responsibility (TPSR) Model in Chinese Middle School: A Mixed-Methods Study

This study examined a six-month implementation of the Teaching Personal and Social Responsibility (TPSR) model in a Chinese middle school. Two Grade 8 classes were involved in the study, both of which were taught by the researcher. A mixed-methods design was employed to examine both the implementation process and the i...

Label-free Industrial Fault Detection via Adversarial Inverse Reinforcement Learning: A System for Run-to-Failure Prognostics

An adversarial inverse reinforcement learning (AIRL) framework that treats MFD as an offline IRL problem, and is the only method achieving non-saturated post-detection consistency across all datasets, while CB baselines fail to detect gradual degradation and reconstruction models collapse into always-anomalous states.

Next-generation electrified powertrain technologies for sustainable mobility: A systematic review of architectures, intelligent control, energy storage, and grid integration

The global imperative to decarbonise road transport has required the automotive and energy sectors to reconceptualise vehicular powertrain engineering across all scales, from atomic-level electrochemistry to fleet-level grid interaction. This PRISMA-compliant systematic review synthesises 120 peer-reviewed publications...

A comprehensive survey on connectivity for IoT sensor networks

This paper presents various feature scopes used in wireless sensor networks (WSNs) and illustrates distinct approaches like relay node placement, efficient communication, machine-learning systems i.e. reinforcement learning, and heuristic techniques.

CoRL: Co-Evolutionary Reinforcement Learning for Adaptive Indirect Prompt-Injection Attacks and Defenses

Tool-augmented language agents are vulnerable to indirect prompt injection (IPI). Unlike direct prompt injection, IPI hides adversarial instructions in untrusted tool outputs and can covertly alter the execution of a legitimate task. Defenses trained on fixed attacks may fail as an attacker changes its strategy, inject...

Temporal-Causal Inference for Reinforcement Learning via Automata Learning

Temporal-Causal Inference for Reinforcement Learning (TCIRL), a framework that jointly learns a control policy and infers the hidden temporal cause of the phase transition and recovers the correct cause DFA and matches the full-information baseline in both domains.

One Step, One Lead: Mitigating Higher-Order Interference in Multi-Domain Reinforcement Learning via Cross-Step Control

OSOL, which designates a focus domain at each iteration, uses the preceding checkpoint footprint to rank token-level rebound risk, and applies a drift-ranked, adaptively scaled correction within the standard GRPO update, is proposed, which suppresses the targeted cross-step output backtracking component.

Noisy-Space Policy Gradient for Diffusion Policies in Offline Reinforcement Learning

A noisy-space action-value (Q-)function that assigns values to diffusion latents through the distribution of executed actions induced by the denoising process is introduced, which admits a precise semantic interpretation and derives a noisy-space policy gradient (NSPG) that optimizes noisy latents using only clean acti...

Do Quantum AIs Dream in Paths? Path-Integral Slow Thinking through Grover Interference

These results establish a Grover-based realization of path-integral slow thinking: the interior target preserves exploratory path diversity, and ensemble-level interference converts it into verified performance.

VD-E2E: A hybrid reinforcement learning approach integrating vehicle-drone perception for end-to-end automated parking

Path-power joint allocation based on improved hierarchical reinforcement learning method for distributed cooperative jamming

Heterogeneous deformation behavior and workability of Mg/Al bimetals during thermomechanical processing: A reinforcement learning-based process regulation method