Marcello Restelli
Reinforcement Learning · 2006–2022
A researcher in the Science corpus: 17 papers; trusts 158 researchers and is trusted by 20 researchers here, as inferred from the citation graph.
Subfields
- Reinforcement Learning — 200 citations into it; #965 of 2,242 by trust here, #1,319 by citations
- Artificial Intelligence — 188 citations into it; #14,879 of 35,345 by trust here, #10,566 by citations
- Computational Theory and Mathematics — 61 citations into it; #5,572 of 12,182 by trust here, #6,798 by citations
- Management Science and Operations Research — 51 citations into it; #4,947 of 11,946 by trust here, #3,705 by citations
- Optimization & Training — 40 citations into it
- Control and Systems Engineering — 30 citations into it; #6,622 of 8,257 by trust here, #6,083 by citations
- Statistics and Probability — 29 citations into it
- Computer Vision and Pattern Recognition — 16 citations into it
Whom Marcello Restelli trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Jan Peters
- Sham M. Kakade
- Richard S. Sutton
- Ronald J. Williams
- Jonathan Baxter
- Hado van Hasselt
- Stefan Schaal
- Andrew Barto
- Dimitri P. Bertsekas
- Geoffrey E. Hinton
Who trusts Marcello Restelli overall
- Matteo Papini
- Matteo Pirotta
- Kamil Andrzej Ciosek
- Rodrigo Nogueira
- Alessandro Lazaric
- Shimon Whiteson
- Matthew E. Taylor
- Yu Zhang
- Yusen Zhan
- Carlo D'Eramo
Papers from Marcello Restelli's perspective
Ordered by how strongly the researchers Marcello Restelli trusts endorse each paper. Same corpus, a different order for every researcher.
- ImageNet classification with deep convolutional neural networks
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Gradient-based learning applied to document recognition
- Long Short-Term Memory
- Attention is All you Need (Neural Information Processing Systems)
- Reinforcement Learning: An Introduction
- ImageNet: A large-scale hierarchical image database
- Learning Multiple Layers of Features from Tiny Images
- Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
Their papers
- Transfer of samples in batch reinforcement learning (2008) — 200 citations
- Policy gradient in Lipschitz Markov Decision Processes (2015) — 101 citations
- Sharing Knowledge in Multi-Task Deep Reinforcement Learning (2020) — 156 citations
- Policy Optimization via Importance Sampling (2018) — 103 citations
- Stochastic Variance-Reduced Policy Gradient (2018) — 213 citations
- Sparse multi-task reinforcement learning (2014) — 80 citations
- Lifelong Hyper-Policy Optimization with Multiple Importance Sampling Regularization (2021) — 11 citations
- Learning to cooperate in multi-agent social dilemmas (2006) — 59 citations
- Goal-Directed Planning via Hindsight Experience Replay (2022) — 15 citations
- A practical guide to multi-objective reinforcement learning and planning (2021) — 587 citations
- Adaptive Batch Size for Safe Policy Gradients (2017) — 37 citations
- Estimating the Maximum Expected Value in Continuous Reinforcement Learning Problems (2017) — 20 citations
- Boosted Fitted Q-Iteration (2017) — 49 citations
- Estimating Maximum Expected Value through Gaussian Approximation (2016) — 46 citations
- Adaptive Step-Size for Policy Gradient Methods (2013) — 79 citations
- Safe Policy Iteration (2013) — 112 citations
- Fitted Policy Search: Direct Policy Search using a Batch Reinforcement Learning Approach (2010) — 5 citations