Imagen de Portada

A Comparative Study of Deep Reinforcement Learning for Crop Production Management

Fuente: arXiv

Guardado en:

Detalles Bibliográficos
Autores principales:	Balderas, Joseph, Chen, Dong, Huang, Yanbo, Wang, Li, Li, Ren-Cang
Formato:	Preprint
Publicado:	2024
Materias:	Systems and Control Machine Learning
Acceso en línea:	Acceder al recurso
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

Ejemplares similares

Integrating Reinforcement Learning and Large Language Models for Crop Production Process Management Optimization and Control through A New Knowledge-Based Deep Learning Paradigm
por: Chen, Dong, et al.
Publicado: (2024)

Federated Multi-Agent Deep Reinforcement Learning Approach via Physics-Informed Reward for Multi-Microgrid Energy Management
por: Li, Yuanzheng, et al.
Publicado: (2022)

Attentive Convolutional Deep Reinforcement Learning for Optimizing Solar-Storage Systems in Real-Time Electricity Markets
por: Li, Jinhao, et al.
Publicado: (2024)

Enhancing Cyber-Resilience in Integrated Energy System Scheduling with Demand Response Using Deep Reinforcement Learning
por: Li, Yang, et al.
Publicado: (2023)

Temporal-Aware Deep Reinforcement Learning for Energy Storage Bidding in Energy and Contingency Reserve Markets
por: Li, Jinhao, et al.
Publicado: (2024)

Deep Reinforcement Learning-driven Cross-Community Energy Interaction Optimal Scheduling
por: Li, Yang, et al.
Publicado: (2023)

QSTAformer: A Quantum-Enhanced Transformer for Robust Short-Term Voltage Stability Assessment against Adversarial Attacks
por: Li, Yang, et al.
Publicado: (2025)

A Multiobjective Reinforcement Learning Framework for Microgrid Energy Management
por: Liu, M. Vivienne, et al.
Publicado: (2023)

Action-Attentive Deep Reinforcement Learning for Autonomous Alignment of Beamlines
por: Wang, Siyu, et al.
Publicado: (2024)

Nuclear Microreactor Control with Deep Reinforcement Learning
por: Tunkle, Leo, et al.
Publicado: (2025)

DeepSafeMPC: Deep Learning-Based Model Predictive Control for Safe Multi-Agent Reinforcement Learning
por: Wang, Xuefeng, et al.
Publicado: (2024)

Comparative Field Deployment of Reinforcement Learning and Model Predictive Control for Residential HVAC
por: Mulayim, Ozan Baris, et al.
Publicado: (2025)

Integration of Multi-Mode Preference into Home Energy Management System Using Deep Reinforcement Learning
por: Sumayli, Mohammed, et al.
Publicado: (2025)

Reinforcement Learning Based Bidding Framework with High-dimensional Bids in Power Markets
por: Liu, Jinyu, et al.
Publicado: (2024)

Reinforcement Learning-based Control via Y-wise Affine Neural Networks: Comparative Case Studies for Chemical Processes
por: Braniff, Austin, et al.
Publicado: (2026)

Kernel-Based Safe Exploration in Deep Reinforcement Learning
por: Majumdar, Rupak, et al.
Publicado: (2026)

Green Energy Management for Sustainable Data Centers Using Deep Reinforcement Learning
por: Bahi, Abderaouf, et al.
Publicado: (2025)

Hierarchical Deep Reinforcement Learning Framework for Multi-Year Asset Management Under Budget Constraints
por: Fard, Amir, et al.
Publicado: (2025)

Investigating the Impact of Choice on Deep Reinforcement Learning for Space Controls
por: Hamilton, Nathaniel, et al.
Publicado: (2024)

AoI-Aware Task Offloading and Transmission Optimization for Industrial IoT Networks: A Branching Deep Reinforcement Learning Approach
por: Chen, Yuang, et al.
Publicado: (2025)

Power Allocation for Delay Optimization in Device-to-Device Networks: A Graph Reinforcement Learning Approach
por: Fang, Hao, et al.
Publicado: (2025)

When to Trust Your Data: Enhancing Dyna-Style Model-Based Reinforcement Learning With Data Filter
por: Li, Yansong, et al.
Publicado: (2024)

Infinite-Horizon Reach-Avoid Zero-Sum Games via Deep Reinforcement Learning
por: Li, Jingqi, et al.
Publicado: (2022)

Learning Coverage Paths in Unknown Environments with Deep Reinforcement Learning
por: Jonnarth, Arvi, et al.
Publicado: (2023)

Safe Multi-Agent Reinforcement Learning with Convergence to Generalized Nash Equilibrium
por: Li, Zeyang, et al.
Publicado: (2024)

DOA: A Degeneracy Optimization Agent with Adaptive Pose Compensation Capability based on Deep Reinforcement Learning
por: Li, Yanbin, et al.
Publicado: (2025)

Optimism as Risk-Seeking in Multi-Agent Reinforcement Learning
por: Zhang, Runyu, et al.
Publicado: (2025)

Neighbor-Aware Reinforcement Learning for Mixed Traffic Optimization in Large-scale Networks
por: Islam, Iftekharul, et al.
Publicado: (2024)

Application of Deep Reinforcement Learning to Event-Triggered Control for Networked Artificial Pancreas Systems
por: Ikemoto, Junya, et al.
Publicado: (2026)

Multi-Target Radar Search and Track Using Sequence-Capable Deep Reinforcement Learning
por: Ewers, Jan-Hendrik, et al.
Publicado: (2025)

Recurrent Auto-Encoders for Enhanced Deep Reinforcement Learning in Wilderness Search and Rescue Planning
por: Ewers, Jan-Hendrik, et al.
Publicado: (2025)

Economic Battery Storage Dispatch with Deep Reinforcement Learning from Rule-Based Demonstrations
por: Sage, Manuel, et al.
Publicado: (2025)

Reverse Flow Matching: A Unified Framework for Online Reinforcement Learning with Diffusion and Flow Policies
por: Li, Zeyang, et al.
Publicado: (2026)

Explainable Reinforcement Learning-based Home Energy Management Systems using Differentiable Decision Trees
por: Gokhale, Gargya, et al.
Publicado: (2024)

Multi-hop Upstream Anticipatory Traffic Signal Control with Deep Reinforcement Learning
por: Li, Xiaocan, et al.
Publicado: (2024)

Training Task Reasoning LLM Agents for Multi-turn Task Planning via Single-turn Reinforcement Learning
por: Hu, Hanjiang, et al.
Publicado: (2025)

A Survey on Reinforcement Learning in Aviation Applications
por: Razzaghi, Pouria, et al.
Publicado: (2022)

Adaptive Decentralized Federated Learning in Energy and Latency Constrained Wireless Networks
por: Yan, Zhigang, et al.
Publicado: (2024)

Proximal Reliability Optimization for Reinforcement Learning
por: Patwardhan, Narendra, et al.
Publicado: (2019)

Global Rewards in Multi-Agent Deep Reinforcement Learning for Autonomous Mobility on Demand Systems
por: Hoppe, Heiko, et al.
Publicado: (2023)