A reinforcement learning (RL) framework written in C++ as a building block for quantum network control. The code implements the MDPs from the work titled "Reinforcement Learning for Quantum Network Control with Application-Driven Objectives," using policy gradients and the chain rule to optimize arbitrary non-linear ob...
Eric Yau, Alexandra Burushkina, Francisco Ferreira da Silva et al.· 4TU.ResearchData· 0 citations
Cloud-edge systems for controllable diffusion generation typically upload the raw reference image, although ControlNet consumes only a sparse structural condition derived from it. We present a cloud-edge framework for directed cultural pattern generation in which the edge extracts the condition map, compresses it lossl...
In our earlier methodology paper, we introduced a hierarchical framework combining graph compression, Diffusion Convolutional Recurrent Neural Networks (DCRNNs), and Multi-Agent Reinforcement Learning (MARL) to approximate Bellman’s optimality principle for real-time energy system control, validated using Palm Springs,...
W. B. Lee, Anthony G. Constantinides· Electronics· 0 citations
Classical input design minimises the covariance of the estimated model parameters. For nonlinear black-box models, however, the dominant error is the bias induced by the model structure over the region of interest, which motivates space-filling strategies seeking to cover the system state space as uniformly as possible...
Charly Witmeur, Gonçalo G. Cruz, Maarten Schoukens et al.· Zenodo (CERN European Organi...· 0 citations
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This method formalizes the navigation task as a semiMarkov decision process and constructs a two-layer decision architecture with collaboration between a high-level manager and a low-level worker with collaboration between a high-level manager and a low-level worker.
Qi-Ming Chen· International Conference on...· 0 citations
Evolution by natural selection produces the appearance of design in minds, brains, and behavior. While rational models of cognition are usually considered to be consistent with evolution, they are rarely derived from distinctively evolutionary assumptions. Bayesian modeling, reinforcement learning, rational analysis, a...
Cameron Rouse Turner, Thomas L. Griffiths· 0 citations
Reinforcement learning (RL) offers a principled framework for adaptive trading under the nonstationary dynamics of financial markets, yet the interaction between state representation and reward design remains poorly understood. This study represents a controlled empirical comparison of six RL trading agents trained via...
Erik Bloomquist, Cátia Silva· UF Journal of Undergraduate...· 0 citations
Safety-Aware DQN Variants for AV Collision Avoidance This release contains the research software and experiment code supporting the study "Safety-Aware Deep Q-Network Variants for Lightweight Sensor-Based Collision Avoidance in Autonomous Vehicles." The project compares three Deep Reinforcement Learning variants, namel...
Richky Abednego· Zenodo (CERN European Organi...· 0 citations
Data supporting “A reinforcement-learning policy-transfer framework for computer-vision-based occupant-centric heating and natural ventilation control” submitted to Building Research & Information.
This poster presents a multi-agent reinforcement learning framework for coordinating household and community battery energy storage systems. The proposed approach considers household heterogeneity, multi-level objectives, internal pricing, and coordinated battery operation within a shared community energy system.
Cao Tram Anh Hoang, Chun Ouyang· Zenodo (CERN European Organi...· 0 citations
(c) 2026 Pranay Mahendrakar. Licensed under CC BY 4.0. An agent that stores what happens to it must decide what is worth storing and, later, what is worth reading back. The most-copied mechanism for the first decision is a scalar written at storage time: a language model is shown a record and asked how important it is....
Pranay M. Mahendrakar· Zenodo (CERN European Organi...· 0 citations
This reproducibility archive contains the MATLAB/Simulink models, five frozen DDPG agents, training and evaluation scripts, harmonized PI versus RL-assisted PI comparisons, deterministic and randomized robustness studies, computational-burden and multirate analyses, numerical-convergence studies, cycle-averaged local-s...
Sarah Abdul Sattar Mahdi, Mohammed Kh. AL-NUSSAIRI· Zenodo (CERN European Organi...· 0 citations
Prabha Jha used her experience with MIT Open Learning’s program for senior executives to develop AI solutions for her employer, Verizon, and to write a book.
Assistant Professor Benjamin Lindquist studies how engineers deliberately injected randomness into early AI, giving rise to deep-learning models that sometimes hallucinate.
