reinforcement learning

Code and data for a controlled study of batch reinforcement learning for stepwise composition of strip-packing heuristic components

Python code and data for stepwise composition of classic best-fit-family components with tree-based Fitted Q Iteration: the configurable constructive engine, FQI/Double-DQN training pipelines, pre-registered control arms, all experiment runners, benchmark instances, per-run raw results (CSV), and the trained per-fold m...

Fisher Information Geometry, Reinforcement Learning, and Two-Layer Stochastic Approximation: From Prefill–Decode Dynamics to Multiscale Coupled Systems

Proactive PEDMS: a hybrid priority-aware reinforcement learning scheduler for microservice orchestration in edge-cloud environments

Crowding governs resistance confinement and guides the improvement of adaptive therapy in a 2D melanoma model system

The emergence of treatment resistant mutants is one of the primary contributors to therapy failure in cancer. Evolution-based adaptive therapies aim to exploit the competition between sensitive and resistant cells to prolong therapeutic control. However, in the densely packed tissues of solid tumors, competition is not...

Reinforcement learning-enhanced three-layer hybrid BESS-hydrogen storage in renewable microgrids with adaptive reliability thresholds and degradation awareness

In low-carbon microgrids, hybrid battery-hydrogen storage is crucial for managing renewable energy variability. However, current optimization techniques overlook battery degradation and rely on static reliability standards that fail to adapt to changing system conditions. This paper presents a three-layer framework enh...

Model-free in situ training of diffractive optical processors via proximal policy optimization for stable and sample-efficient learning

We present a model-free in situ training framework for diffractive optical processors based on Proximal Policy Optimization (PPO), a reinforcement learning algorithm designed for stable and sample-efficient policy updates. By enabling multiple optimization steps using the same set of captured optical measurements while...

Dataset-Driven Exploratory Deep Reinforcement Learning Using SINDy-Based Data Augmentation from Limited Indoor Thermal Measurements

This study examines the feasibility of combining Sparse Identification of Nonlinear Dynamical Systems (SINDy)-based data augmentation with an Actor–Critic deep reinforcement learning (DRL) model in a dataset-driven exploratory policy-learning configuration under limited-data conditions. The modeling and training datase...

Data and results for: Deep reinforcement learning control of a grid-tied microgrid with a heat pump and PCM thermal storage

This dataset accompanies the paper "Deep reinforcement learning control of a grid-tied microgrid with a heat pump and PCM thermal storage: a comparison with rule-based control in an experimentally calibrated simulation" (under review). The code is at https://github.com/fred-stanford/pcm-tes-microgrid-rl. The study trai...

Performance evaluation and optimization of a mobile robot path-planning model based on reinforcement learning

Autonomous navigation in mobile robotics faces significant challenges in dynamic, unstructured environments, as re-planning strategies often lack time efficiency when recomputing around new obstacles. Traditional path-planning algorithms typically rely on static, pre-mapped environments and rigid geometric rules, which...

SGMoE-ScMoRL: Sparsely Gated Mixture of Experts Reinforcement Learning for Single-Cell Multi-Omics Integration

Explainable graph-based meta-reinforcement learning for cognitive UAV swarms under partial observability and adaptive jamming

Autotaxis of a run-and-tumble agent via reinforcement learning