Finbarr Timbers

Artificial Intelligence · 2019

A researcher in the Science corpus: 2 papers; trusts 11 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 60 citations into it
• Reinforcement Learning — 60 citations into it
• Game & Decision Theory — 58 citations into it
• Management Science and Operations Research — 36 citations into it
• Economics and Econometrics — 27 citations into it
• Optimization & Training — 12 citations into it

Whom Finbarr Timbers trusts overall

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

1. Martin A. Zinkevich
2. Richard S. Sutton
3. Michael H. Bowling

Papers from Finbarr Timbers's perspective

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

1. Reinforcement Learning: An Introduction
2. Asynchronous Methods for Deep Reinforcement Learning
3. Human-level control through deep reinforcement learning
4. The Arcade Learning Environment: An Evaluation Platform for General Agents
5. Markov Games as a Framework for Multi-Agent Reinforcement Learning
6. Regret Minimization in Games with Incomplete Information
7. Mastering the game of Go with deep neural networks and tree search
8. Bandit Based Monte-Carlo Planning
9. A Course in Game Theory
10. Fast algorithms for finding randomized strategies in game trees

Their papers

• Computing Approximate Equilibria in Sequential Adversarial Games by Exploitability Descent (2019) — 93 citations
• OpenSpiel: A Framework for Reinforcement Learning in Games (2019) — 323 citations