reinforcement learning

Microgrid energy management optimization using PPO with curriculum learning trained in virtual simulation environment

Microgrid energy management systems (EMS) require real-time, economically optimal, and safe dispatch strategies under high renewable uncertainty. While Deep Reinforcement Learning (DRL) offers promising online decision-making capabilities, standard DRL algorithms struggle with the “cold start” problem, slow convergence...

Long-term engagement-oriented short video recommendation via deep reinforcement learning

Short video platforms require recommendation models that can adapt to rapidly changing user interests while optimizing engagement beyond immediate click feedback. Existing collaborative filtering and supervised deep learning methods are effective for static preference matching and short-term click-through-rate predicti...

Strategic flight planning and conflict management for urban air mobility operations: a mission preference-constrained multiagent reinforcement learning approach

As an emerging low-altitude transportation paradigm, urban air mobility (UAM) is envisioned to support high-density and demand-driven operations involving diverse and flexible mission requests. However, the imbalance between limited urban airspace resources and growing operational demands inevitably causes frequent fli...

Prepubertal ovariectomy alters dorsomedial striatum indirect pathway neuron excitability and explore/exploit balance in female mice

Decision-making circuits are modulated across life stages (e.g. juvenile, adolescent, or adult)—as well as on the shorter timescale of reproductive cycles in females—to meet changing environmental and physiological demands. Ovarian hormone signaling may contribute to this flexibility by regulating neural circuits invol...

GenPage: Towards End-to-End Generative Homepage Construction at Netflix

We present GenPage, an end-to-end generative approach to Netflix homepage construction that replaces the traditional multi-stage recommender stack with a single transformer. GenPage treats the user and request context as a prompt and autoregressively generates the entire structured, multi-row homepage as the response....

Long-term User Engagement Optimization through Model-agnostic Downstream Rewards Learning

As recommender systems mature in the past few years, their optimization objectives have evolved from a primary focusing on short-term behavioral signals to a broader emphasis on long-term user engagement and retention. However, directly optimizing retention is difficult because return signals are sparse, delayed, and o...

Beyond Metrics: A Human Validation Study of Procedural Personas in a Continuous Game Arena

This dataset accompanies the paper “Beyond Metrics: A Human Validation Study of Procedural Personas in a Continuous Game Arena”. It contains the gameplay videos used as stimuli in the user study, the training plots for the five reinforcement‑learning personas, the raw survey data, and the complete statistical analysis...

SAGE: Sequence-level Adaptive Gradient Evolution for Generative Recommendation

Generative recommenders increasingly train Semantic-ID models to autoregressively generate length-L slates, aligned to user feedback by reinforcement learning. Gradient-Bounded Policy Optimization (GBPO) stabilizes this stage with a BCE-stable asymmetric gradient bound, but its clipping boundary is computed per sample...

E-MRL: Cross-View Aligned Evidence-Driven Multimodal Reinforcement Learning for Reliable 3D Tumor Analysis

LEME: open large language models for ophthalmology with advanced reasoning and clinical validation

The rising prevalence of eye diseases poses a growing public health burden. Large language models (LLMs) offer a promising path to reduce documentation workload and support clinical decision-making. However, few have been tailored for ophthalmology, and most evaluations focus mainly on knowledge-based QA without clinic...

Multi-objective optimization of best management practices in agricultural watersheds using single-step deep reinforcement learning

A machine learning framework for automated planning of hybrid energy systems using generalizable deep reinforcement learning