Renzhi Lu

Electrical and Electronic Engineering · 2018–2019

A researcher in the Science corpus: 3 papers; trusts 15 researchers and is trusted by 10 researchers here, as inferred from the citation graph.

Subfields

• Electrical and Electronic Engineering — 27 citations into it; #3,536 of 16,539 by trust here, #10,154 by citations
• Reinforcement Learning — 13 citations into it
• Control and Systems Engineering — 10 citations into it; #3,471 of 8,257 by trust here, #4,722 by citations

Whom Renzhi Lu trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

1. Seung Ho Hong
2. J. Gao
3. Christopher J. C. H. Watkins
4. A. Dagoumas
5. Mengmeng Yu
6. Ming Jin
7. Peter S. Dayan
8. Richard S. Sutton
9. Rafal Weron
10. John N. Tsitsiklis

Who trusts Renzhi Lu overall

1. Seung Ho Hong
2. Xu Xu
3. I. Antonopoulos
4. A. Perera
5. Liang Yu
6. Youwei Jia
7. Mengmeng Yu
8. Valentin Robu
9. Parameswaran Kamalaruban
10. Yan Xu

Papers from Renzhi Lu's perspective

Ordered by how strongly the researchers Renzhi Lu trusts endorse each paper. Same corpus, a different order for every researcher.

1. Human-level control through deep reinforcement learning
2. Reinforcement Learning: An Introduction
3. Electricity price forecasting: A review of the state-of-the-art with a look into the future
4. A Comprehensive Survey of Multiagent Reinforcement Learning
5. The role of regulatory reforms, market changes, and technology development to make demand response a viable resource in meeting energy challenges
6. Recent advances in electricity price forecasting: A review of probabilistic forecasting
7. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
8. Gradient-based learning applied to document recognition
9. Day-ahead electricity price forecasting via the application of artificial neural network based models
10. ImageNet classification with deep convolutional neural networks

Their papers

• Demand Response for Home Energy Management Using Reinforcement Learning and Artificial Neural Network (2019) — 334 citations
• A Dynamic pricing demand response algorithm for smart grid: Reinforcement learning approach (2018) — 389 citations
• Incentive-based demand response for smart grid with reinforcement learning and deep neural network (2019) — 347 citations