A New Error Temporal Difference Algorithm for Deep Reinforcement Learning in Microgrid Optimization
Fuente:
arXiv
Saved in:
| Main Authors: | Yao, Fulong, Zhao, Wanqing, Forshaw, Matthew |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
A Holistic Power Optimization Approach for Microgrid Control Based on Deep Reinforcement Learning
by: Yao, Fulong, et al.
Published: (2024)
by: Yao, Fulong, et al.
Published: (2024)
A Self-organizing Interval Type-2 Fuzzy Neural Network for Multi-Step Time Series Prediction
by: Yao, Fulong, et al.
Published: (2024)
by: Yao, Fulong, et al.
Published: (2024)
Deep Reinforcement Learning and The Tale of Two Temporal Difference Errors
by: Rojas, Juan Sebastian, et al.
Published: (2026)
by: Rojas, Juan Sebastian, et al.
Published: (2026)
Diffusion-Modeled Reinforcement Learning for Carbon and Risk-Aware Microgrid Optimization
by: Zhao, Yunyi, et al.
Published: (2025)
by: Zhao, Yunyi, et al.
Published: (2025)
Generalized Gaussian Temporal Difference Error for Uncertainty-aware Reinforcement Learning
by: Kim, Seyeon, et al.
Published: (2024)
by: Kim, Seyeon, et al.
Published: (2024)
Discovering Temporally-Aware Reinforcement Learning Algorithms
by: Jackson, Matthew Thomas, et al.
Published: (2024)
by: Jackson, Matthew Thomas, et al.
Published: (2024)
BET: Explaining Deep Reinforcement Learning through The Error-Prone Decisions
by: Liu, Xiao, et al.
Published: (2024)
by: Liu, Xiao, et al.
Published: (2024)
Temporal Difference Learning with Compressed Updates: Error-Feedback meets Reinforcement Learning
by: Mitra, Aritra, et al.
Published: (2023)
by: Mitra, Aritra, et al.
Published: (2023)
A Survey of Temporal Credit Assignment in Deep Reinforcement Learning
by: Pignatelli, Eduardo, et al.
Published: (2023)
by: Pignatelli, Eduardo, et al.
Published: (2023)
Is Exploration or Optimization the Problem for Deep Reinforcement Learning?
by: Berseth, Glen
Published: (2025)
by: Berseth, Glen
Published: (2025)
Optimizing Automatic Differentiation with Deep Reinforcement Learning
by: Lohoff, Jamie, et al.
Published: (2024)
by: Lohoff, Jamie, et al.
Published: (2024)
Discerning Temporal Difference Learning
by: Ma, Jianfei
Published: (2023)
by: Ma, Jianfei
Published: (2023)
Backstepping Temporal Difference Learning
by: Lim, Han-Dong, et al.
Published: (2023)
by: Lim, Han-Dong, et al.
Published: (2023)
MiWaves Reinforcement Learning Algorithm
by: Ghosh, Susobhan, et al.
Published: (2024)
by: Ghosh, Susobhan, et al.
Published: (2024)
Integrating Reinforcement Learning and Model Predictive Control with Applications to Microgrids
by: da Silva, Caio Fabio Oliveira, et al.
Published: (2024)
by: da Silva, Caio Fabio Oliveira, et al.
Published: (2024)
Constraint-Conditioned Policy Optimization for Versatile Safe Reinforcement Learning
by: Yao, Yihang, et al.
Published: (2023)
by: Yao, Yihang, et al.
Published: (2023)
Temporal-Difference Variational Continual Learning
by: Melo, Luckeciano C., et al.
Published: (2024)
by: Melo, Luckeciano C., et al.
Published: (2024)
Gradient Iterated Temporal-Difference Learning
by: Vincent, Théo, et al.
Published: (2026)
by: Vincent, Théo, et al.
Published: (2026)
XQC: Well-conditioned Optimization Accelerates Deep Reinforcement Learning
by: Palenicek, Daniel, et al.
Published: (2025)
by: Palenicek, Daniel, et al.
Published: (2025)
SafeAdapt: Provably Safe Policy Updates in Deep Reinforcement Learning
by: Anisimov, Maksim, et al.
Published: (2026)
by: Anisimov, Maksim, et al.
Published: (2026)
The Definitive Guide to Policy Gradients in Deep Reinforcement Learning: Theory, Algorithms and Implementations
by: Lehmann, Matthias
Published: (2024)
by: Lehmann, Matthias
Published: (2024)
Beyond Error-Based Optimization: Experience-Driven Symbolic Regression with Goal-Conditioned Reinforcement Learning
by: Sun, Jianwen, et al.
Published: (2026)
by: Sun, Jianwen, et al.
Published: (2026)
A Variance Minimization Approach to Temporal-Difference Learning
by: Chen, Xingguo, et al.
Published: (2024)
by: Chen, Xingguo, et al.
Published: (2024)
Bounding-Box Inference for Error-Aware Model-Based Reinforcement Learning
by: Talvitie, Erin J., et al.
Published: (2024)
by: Talvitie, Erin J., et al.
Published: (2024)
Enhancing Microgrid Performance Prediction with Attention-based Deep Learning Models
by: Maddineni, Vinod Kumar, et al.
Published: (2024)
by: Maddineni, Vinod Kumar, et al.
Published: (2024)
Can Learned Optimization Make Reinforcement Learning Less Difficult?
by: Goldie, Alexander David, et al.
Published: (2024)
by: Goldie, Alexander David, et al.
Published: (2024)
Demystifying the Recency Heuristic in Temporal-Difference Learning
by: Daley, Brett, et al.
Published: (2024)
by: Daley, Brett, et al.
Published: (2024)
Diversity Optimization for Travelling Salesman Problem via Deep Reinforcement Learning
by: Li, Qi, et al.
Published: (2025)
by: Li, Qi, et al.
Published: (2025)
Efficient Deep Reinforcement Learning with Predictive Processing Proximal Policy Optimization
by: Küçükoğlu, Burcu, et al.
Published: (2022)
by: Küçükoğlu, Burcu, et al.
Published: (2022)
Deep Reinforcement Learning for Inventory Networks: Toward Reliable Policy Optimization
by: Alvo, Matias, et al.
Published: (2023)
by: Alvo, Matias, et al.
Published: (2023)
Conformal Symplectic Optimization for Stable Reinforcement Learning
by: Lyu, Yao, et al.
Published: (2024)
by: Lyu, Yao, et al.
Published: (2024)
Deep Reinforcement Learning from Hierarchical Preference Design
by: Bukharin, Alexander, et al.
Published: (2023)
by: Bukharin, Alexander, et al.
Published: (2023)
A Temporally Correlated Latent Exploration for Reinforcement Learning
by: Oh, SuMin, et al.
Published: (2024)
by: Oh, SuMin, et al.
Published: (2024)
Consciousness-Inspired Spatio-Temporal Abstractions for Better Generalization in Reinforcement Learning
by: Zhao, Mingde, et al.
Published: (2023)
by: Zhao, Mingde, et al.
Published: (2023)
ULTHO: Ultra-Lightweight yet Efficient Hyperparameter Optimization in Deep Reinforcement Learning
by: Yuan, Mingqi, et al.
Published: (2025)
by: Yuan, Mingqi, et al.
Published: (2025)
Interpretable Deep Reinforcement Learning for Element-level Bridge Life-cycle Optimization
by: Moayyedi, Seyyed Amirhossein, et al.
Published: (2026)
by: Moayyedi, Seyyed Amirhossein, et al.
Published: (2026)
Advancing Investment Frontiers: Industry-grade Deep Reinforcement Learning for Portfolio Optimization
by: Ndikum, Philip, et al.
Published: (2024)
by: Ndikum, Philip, et al.
Published: (2024)
Deep Multi-Objective Reinforcement Learning for Utility-Based Infrastructural Maintenance Optimization
by: van Remmerden, Jesse, et al.
Published: (2024)
by: van Remmerden, Jesse, et al.
Published: (2024)
Multiobjective Hydropower Reservoir Operation Optimization with Transformer-Based Deep Reinforcement Learning
by: Wu, Rixin, et al.
Published: (2023)
by: Wu, Rixin, et al.
Published: (2023)
Is Temporal Difference Learning the Gold Standard for Stitching in RL?
by: Bortkiewicz, Michał, et al.
Published: (2025)
by: Bortkiewicz, Michał, et al.
Published: (2025)
Similar Items
-
A Holistic Power Optimization Approach for Microgrid Control Based on Deep Reinforcement Learning
by: Yao, Fulong, et al.
Published: (2024) -
A Self-organizing Interval Type-2 Fuzzy Neural Network for Multi-Step Time Series Prediction
by: Yao, Fulong, et al.
Published: (2024) -
Deep Reinforcement Learning and The Tale of Two Temporal Difference Errors
by: Rojas, Juan Sebastian, et al.
Published: (2026) -
Diffusion-Modeled Reinforcement Learning for Carbon and Risk-Aware Microgrid Optimization
by: Zhao, Yunyi, et al.
Published: (2025) -
Generalized Gaussian Temporal Difference Error for Uncertainty-aware Reinforcement Learning
by: Kim, Seyeon, et al.
Published: (2024)