reinforcement learning

Increased monetary incentives enhance goal-directed reinforcement learning but fail to suppress automatic learning processes

Adaptive reinforcement learning requires assigning outcomes to the features of actions that causally determine them. Yet humans also learn reward associations with action features that are explicitly known to be outcome-irrelevant, and these associations can bias subsequent choices. Whether such maladaptive learning is...

Representative view selection and hard view learning for multiview 3D model classification

View-based 3D model classification benefits from mature 2D visual backbones, but dense multi-view rendering usually contains many repeated observations. This paper therefore focuses on two practical questions: how to keep a compact set of representative views, and how to make the classifier learn more from difficult vi...

Machine learning-based energy management and bidirectional power control for battery-fed electric vehicle traction drive

Efficient energy management in a battery-fed electric vehicle (EV) traction system requires coordinated control of propulsion power, battery operating conditions, Direct Current (DC)-link voltage, and regenerative braking under rapidly changing driving conditions. This study develops a machine learning (ML)-based ene...

Reinforcement and Deep Learning Approaches to Dynamic Cache Optimization

With explosive data-driven application growth and increasing complexity of contemporary computing environments, conventional static cache management methods fall short more and more. This chapter discusses how reinforcement learning (RL) and deep learning (DL) models are disrupting cache optimization by making caching...

Calibrated Decisions Are Not Calibrated Probabilities: An Exact-Target Audit of Jev and Three Open Decision Models

Decision models answer typed questions with probabilities instead of text, and their main selling point is that those probabilities can be trusted. TypeSafe says its Jev model, trained with an unpublished method called Reinforcement Learning for Calibrated Decisions (RLCD), returns "epistemically honest" probabilities....

TARL-GBS: research on a two-level agent reinforcement learning algorithm for government-driven blockchain sharding

Government blockchain exhibits distinct characteristics including diverse transaction types, stringent permission hierarchies, and complex cross-departmental approval processes. Existing sharding scheduling mechanisms often lead to issues such as unauthorized downgrading of high-access transactions and missing approval...

YouTube Dominates IMO 2025/2026 Search; No Math Content Found — E8 Intelligence Research

FINDING: The search results are dominated by YouTube titles and metadata for IMO 2025/2026 problems, with no actual mathematical content extracted. The only substantive item is an arXiv paper on robotic garment folding (LeHome Challenge 2026), which is unrelated to olympiad mathematics. | MATH: No equations, constants,...

Successor representation supports structural learning of syllable sequences

Humans efficiently learn the temporal structure of speech, yet the underlying cognitive mechanisms remain unclear. Recent research in visuospatial sequential memory has proposed the successor representation (SR), important in reinforcement learning, which encodes multi-step transitional relationships. To test whether S...

A Concise Framework for AI-Driven Blockchain: Integrating DRL Consensus and GNN Security Auditing

This research introduces a novel, high-performance hybrid framework merging Deep Reinforcement Learning (DRL) for dynamic consensus optimization with Graph Neural Networks (GNN) for advanced smart contract security auditing. Traditional blockchain architectures frequently struggle with balancing scalability and securit...

Am I Still Coupled to My Environment? Task-agnostic Runtime Telemetry from the Reinforcement-learning Interaction Loop

A TSP solving model based on multiscale feature fusion and adaptive context awareness

Transformer-based deep reinforcement learning for the Traveling Salesman Problem (TSP) often struggles to capture spatial topology and avoid local optima. To address this, we propose a novel model featuring a Multi-Scale Grid Attention Encoder (MSGAE) to fuse local and global spatial features, alongside a bottleneck-en...

Adaptive Probabilistic Shielding by Learning MDPs for Safe Reinforcement Learning