Results 111 to 120 of about 6,522,305 (296)

RL: Generic reinforcement learning codebase in TensorFlow [PDF]

Journal of Open Source Software, 2019
Bryan M. Li, Alexander I. Cowen-Rivers, Piotr Kozakowski, David Tao, Siddhartha Kamalakara, Nitarshan Rajkumar, Hariharan Sezhiyan, Sicong Huang 0001, Aidan N. Gomez   +8 more
openaire   +2 more sources

SINDy-RL for interpretable and efficient model-based reinforcement learning

Nature Communications
Abstract Deep reinforcement learning (DRL) has shown significant promise for uncovering sophisticated control policies that interact in complex environments, such as stabilizing a tokamak fusion reactor or minimizing the drag force on an object in a fluid flow.
Nicholas Zolman, Christian Lagemann, Urban Fasel, J. Nathan Kutz, Steven L. Brunton   +4 more
openaire   +3 more sources

Robust Reinforcement Learning Control Framework for a Quadrotor Unmanned Aerial Vehicle Using Critic Neural Network

Advanced Intelligent Systems, Volume 7, Issue 3, March 2025.
Quadrotor unmanned aerial vehicle control is critical to maintain flight safety and efficiency, especially when facing external disturbances and model uncertainties. This article presents a robust reinforcement learning control scheme to deal with these challenges.
Yu Cai, Yefeng Yang, Tao Huang, Boyang Li   +3 more
wiley   +1 more source

Adaptive Heuristic Reinforcement Learning (AH-RL) for Safety-Aware Control in Building Energy Management Applications

Eng
Reinforcement learning has shown strong potential for sequential control problems, providing an effective framework for adaptive policy derivation in complex and dynamic environments.
Panagiotis Michailidis, Iakovos Michailidis, Federico Minelli, Hasan Huseyin Coban, Elias Kosmatopoulos   +4 more
doaj   +1 more source

Adapter-RL: Adaptation of Any Agent Using Reinforcement Learning

IEEE Transactions on Games
Deep Reinforcement Learning (DRL) agents frequently face challenges in adapting to tasks outside their training distribution, including issues with over-fitting, catastrophic forgetting and sample inefficiency. Although the application of adapters has proven effective in supervised learning contexts such as natural language processing and computer ...
Yizhao Jin, Gregory G. Slabaugh, Simon M. Lucas   +2 more
openaire   +2 more sources

Exploiting Ferroelectric and Spintronic Dynamics for Neural Network Computation

Advanced Intelligent Systems, EarlyView.
Ferroelectric and spintronic devices, relying on the control of polarization and magnetization, offer intrinsically fast, durable, energy‐efficient, and low‐latency building blocks for analog in‐memory computing. The hysteretic dynamics of an order parameter are leveraged to provide nonvolatile, multistate memory and nonlinear switching. Brain‐inspired
Dashiell Harrison, Kevin Nadaud, Josquin Harrison, Guillaume F. Nataf, Laura Bégon‐Lours   +4 more
wiley   +1 more source

Controlling Cable Driven Parallel Robots Operations—Deep Reinforcement Learning Approach

IEEE Access
Deep Reinforcement Learning (DRL) is a powerful approach for generating control strategies for a variety of complex systems, representing an emerging paradigm in control applications.
Muhammad Kamran Joyo, Abdulmajeed M. Alenezi, Wenfu Xu, Mohamad A. Alawad, Muhmmad Tayyab Yaqoob, Noor Maricar, Sheroz Khan   +6 more
doaj   +1 more source

DrugFlow-RL

Reinforcement learning–optimized checkpoints for DrugFlow-RL, a framework for pocket-conditioned molecular generation with improved synthesizability. These checkpoints were obtained by applying online and offline reinforcement learning to DrugFlow, a ...
Bruno E. Correia (14820990), Rebecca Neeser (15454061), Melih Gokay Yigit (21401204), Adrian W. Dobbelstein (15295136), Junwu Chen (17075047), Philippe Schwaller (17086736)   +5 more
core   +1 more source

Integrating Reinforcement Learning With Explainable Artificial Intelligence for Real‐Time Clinical Decision Support in Dynamic Healthcare Environments

Advanced Intelligent Systems, EarlyView.
A hybrid Reinforcement Learning–Explainable AI framework integrates SHAP and LIME explanations directly into a Deep Q‐Network inference loop for real‐time ICU decision support. Trained on 18 142 mechanically ventilated stays from the eICU database, the system attains 93.0% decision accuracy, 20% fewer errors than RL alone, and a 91% clinician trust ...
Jannatul Ferdaus Disha, MD Faiaz Zaman Dehan, Kazi Md. Tanvir Anzum   +2 more
wiley   +1 more source

Simplified Temporal Consistency Reinforcement Learning

, 2023
Reinforcement learning (RL) is able to solve complex sequential decision-making tasks but is currently limited by sample efficiency and required computation.
Pajarinen, Joni, Zhao, Yi, Zhao, Wenshuai, Boney, Rinu, Kannala, Juho   +4 more
core   +1 more source