reinforcement learning

HoomanAI/Decentralized-Dispatch-Agents: V1.2

Multi-agent reinforcement learning for autonomous emergency medical dispatch on wildfire-degraded road networks. Each vehicle agent keeps a private belief over road passability, reconciles evidence with peers when communication allows, and learns route-level exploration under triage-specific survival-risk budgets.

Nonlinear Inversion of Dissipative Trajectories in Hilbert Spaces via Riccati Differential Matrix Control and Saddle-Node Bifurcation Stabilization

Rigorous formulation and solution of the deterministic nonlinear trajectory inversion problem in four-dimensional Hilbert spaces via the Riccati Differential Equation (DRE) and Continuous Algebraic Riccati Equation (CARE). Derives the optimal LQR feedback law, proves entropy neutralization, and establishes bifurcation...

Dolgun Model: From Willful Choice to Stable States - Dolgun Modeli: İradî Seçimden Kararlı Durumlara

AbstractThis study proposes an original conceptual framework called the Dolgun Model to explain how human lifetransforms over time into specific behavioral orientations and relatively stable states through willful choices andactions. The primary starting point of the model is the idea that human life does not consist o...

HoomanAI/Decentralized-Dispatch-Agents: V1.2

Multi-agent reinforcement learning for autonomous emergency medical dispatch on wildfire-degraded road networks. Each vehicle agent keeps a private belief over road passability, reconciles evidence with peers when communication allows, and learns route-level exploration under triage-specific survival-risk budgets.

UDDPG-Net: Energy- and Fairness-Aware Deep Reinforcement Learning for Resource Allocation in Underwater Communication Networks

This dataset supports the study “UDDPG-Net: Energy- and Fairness-Aware Deep Reinforcement Learning for Resource Allocation in Underwater Communication Networks.” It contains the simulation data, experimental results, and lake-based acoustic measurement records used for evaluating UDDPG-Net. The simulation data cover un...

UDDPG-Net: Energy- and Fairness-Aware Deep Reinforcement Learning for Resource Allocation in Underwater Communication Networks

This dataset supports the study “UDDPG-Net: Energy- and Fairness-Aware Deep Reinforcement Learning for Resource Allocation in Underwater Communication Networks.” It contains the simulation data, experimental results, and lake-based acoustic measurement records used for evaluating UDDPG-Net. The simulation data cover un...

Scheduling unrelated parallel machines with sequence-dependent family setups via deep reinforcement learning-driven rollout and adaptive batching

Jev at the Agent Authorization Boundary: Evaluating TypeSafe's Decision Model and Its Reported RLCD Approach

TypeSafe describes Jev as a typed decision model trained using Reinforcement Learning for Calibrated Decisions (RLCD). We evaluate its behavior at an agent authorization boundary, where software must allow, hold, or deny a proposed action. A historical audit of 150 proposals motivates two separate paired comparisons: J...

Quantum driven virtual power plant optimization using stackelberg reinforcement learning for joint energy and frequency markets

Statistical Rigidity of Wigner-Dyson Eigenvalue Distributions in Gaussian Unitary Ensembles: The Spectral Integrity Operator and Selberg Automorphic Stabilization

Definition and exact evaluation of the Spectral Integrity Operator for Gaussian Unitary Ensemble (GUE) random matrices. Proves the identity T(E/I) = 1.000000 by combining the Wigner surmise normalization with the Dyson-Mehta spectral rigidity statistic at the James Arthur truncation scale. Establishes Selberg automorph...

2026 Interim Teaching Statement Update

Zenodo Description This Interim 2026 Teaching Statement documents the next stage in the evolution of my teaching and learning philosophy, moving from INTEGRATE to SUSTAIN. It is a self-directed biennial update to a philosophy first developed in 2018 through the Harvard University Derek Bok Center for Teaching and Learn...

Diagnosing Estimator Homomorphism Compatibility in Reinforcement Learning with Verifiable Rewards

Reinforcement learning with verifiable rewards (RLVR) evaluates decoded outcomes, whereas policy-gradient estimators operate on concrete trajectories and batch-dependent training state. These two levels need not respect the same equivalence relation. We formalize estimator homomorphism compatibility: when several concr...