reinforcement learning

Can a card-based educational game enhance functional anatomy knowledge in first-year medical students? A mixed-methods study

Anatomy is a foundational yet cognitively demanding subject in undergraduate medical education. Although game-based learning has shown potential to enhance engagement and assessment performance, evidence remains limited for low-cost, single-session, card-based interventions designed to reinforce functional anatomy. Thi...

Cooperative multi-agent reinforcement learning with entangled state representations and copula-based action coordination for urban navigation

The findings demonstrate that combining relational state representations, copula-based dependence modeling, and shared rewards improves coordination, robustness, and stability in multi-agent navigation tasks.

Machine Intelligence on the Edge: Interpretable Cardiac Pattern Localization Using Reinforcement Learning

Abstract Matched filters are widely used to localize signal patterns due to their high efficiency and interpretability. However, their effectiveness deteriorates for low signal-to-noise ratio (SNR) signals, such as those recorded on edge devices, where prominent noise patterns can closely resemble the target within the...

Distributional Reinforcement Learning on Path-dependent Options

Market Risk Prediction Model Based on Sentiment Factors and Deep Integration of Reinforcement Learning

Bit-compressed event-triggered fuzzy control for stochastic time-delay nonlinear systems via reinforcement learning

Deep Reinforcement Learning based Optimal Routing with Software-Defined Networking

An effective method for network congestion avoidance is to optimize the routing. We propose an optimalrouting controlling that combines Deep Reinforcement learning(DRL) based policy optimization andSoftware Defined Network(SDN) based flow control. It uses DRL to learn the optimal network routingpolicy in different traf...

Human outcome-irrelevant value learning is insensitive to its monetary cost

When learning from reinforcement, individuals should assign credit selectively to features of their actions that causally determine outcomes. Yet, even when explicit instructions and extensive experience clarify which action features predict reward, humans assign credit to outcome-irrelevant features, leading to subopt...

Reinforcement learning with experience sharing promotes collective cooperation on higher-order networks

Beyond Traditional Risk Scores: Artificial Intelligence in Coronary Plaque Characterization and Personalized Atherosclerosis Management

This review critically examines the transformative role of artificial intelligence (AI), particularly machine learning (ML) and deep learning (DL) and deep learning (DL), in redefining atherosclerosis management across three interconnected domains and explores how AI-driven predictive models integrate high-dimensional...

Branch-level evaluation of a deep reinforcement learning controller for joint handover, power and spectrum control

Pre-registered, branch-level evaluation of a distributional Dueling N-step DQN with a branching action head for joint handover, transmit power and sub-band control in multi-UE 140 GHz vehicular O-RAN. Includes every pre-registration, including those whose predictions failed.

Deep Reinforcement Learning based Optimal Routing with Software-Defined Networking

An effective method for network congestion avoidance is to optimize the routing. We propose an optimalrouting controlling that combines Deep Reinforcement learning(DRL) based policy optimization andSoftware Defined Network(SDN) based flow control. It uses DRL to learn the optimal network routingpolicy in different traf...