Lara Codecà

Reinforcement Learning · 2019–2021

A researcher in the Science corpus: 2 papers; trusts 17 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Reinforcement Learning — 16 citations into it
• Control and Systems Engineering — 9 citations into it
• Transportation — 8 citations into it
• Artificial Intelligence — 6 citations into it

Whom Lara Codecà trusts overall

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

1. Marco A. Wiering
2. Ronald J. Williams
3. Richard S. Sutton
4. Csaba Szepesvari
5. Shalabh Bhatnagar
6. van der D David Pol
7. Volodymyr Mnih
8. Carlos Guestrin
9. Christopher J. C. H. Watkins
10. Vijay R. Konda

Who trusts Lara Codecà overall

1. Hua Wei

Papers from Lara Codecà's perspective

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

1. Reinforcement Learning: An Introduction
2. Long Short-Term Memory
3. Simple Statistical Gradient-Following Algorithms for Connectionist Reinforcement Learning
4. Human-level control through deep reinforcement learning
5. Asynchronous Methods for Deep Reinforcement Learning
6. Policy Gradient Methods for Reinforcement Learning with Function Approximation
7. Gradient-based learning applied to document recognition
8. Q-learning
9. Actor-Critic Algorithms
10. ImageNet classification with deep convolutional neural networks

Their papers

• A digital twin smart city for citizen feedback (2021) — 441 citations
• Multi-Agent Deep Reinforcement Learning for Large-Scale Traffic Signal Control (2019) — 870 citations