reinforcement learning

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...

Image-Based Reinforcement Learning for Robust Mode Hop Recovery in External-Cavity Diode Lasers

Overview This is the initial public release of the code associated with the paper "Image-Based Reinforcement Learning for Robust Mode Hop Recovery in External-Cavity Diode Lasers." The repository provides a simulation and reinforcement-learning framework for recovering an external-cavity diode laser from mode hops usin...

Robotic Garment Folding via VLA Policy: Engineering, Not Pure Math — E8 Intelligence Research

FINDING: The search results are video titles/abstracts, not actual solutions. No mathematical content is extractable. The only concrete item is an arXiv abstract on robotic garment folding (LeHome Challenge 2026), which is engineering, not pure mathematics. MATH: No equations, constants, or ratios are present in the pr...

Why Transformers Cannot Invent: The Manifold Confinement Problem and the Thermodynamic Engine of Scientific Discovery

The contemporary artificial intelligence research consensus posits that scaling test-time compute, Monte Carlo tree search, and Reinforcement Learning with Verifiable Rewards (RLVR / GRPO) will inevitably culminate in automated, superhuman scientific discovery. In this paper, we demonstrate the structural impossibility...

Trajectory Separation for Scalable Credit Assignment in Cooperative Multi-agent Reinforcement Learning