Finbarr Timbers
Publications by Year
Research Areas
Reinforcement Learning in Robotics, Artificial Intelligence in Games, Advanced Bandit Algorithms Research, Auction Theory and Applications, Adversarial Robustness in Machine Learning
Most-Cited Works
- → Mastering the game of Stratego with model-free multiagent reinforcement learning(2022)138 cited
- → OpenSpiel: A Framework for Reinforcement Learning in Games(2019)106 cited
- → Computing Approximate Equilibria in Sequential Adversarial Games by Exploitability Descent(2019)18 cited
- → Reward-respecting subtasks for model-based reinforcement learning(2023)16 cited
- → Approximate Exploitability: Learning a Best Response(2022)11 cited
- → Student of Games: A unified learning algorithm for both perfect and imperfect information games(2023)9 cited
- → The Advantage Regret-Matching Actor-Critic(2020)8 cited
- → Emergent Bartering Behaviour in Multi-Agent Reinforcement Learning(2022)8 cited
- → Solving Common-Payoff Games with Approximate Policy Iteration(2021)6 cited