AIConfPaper
← Search

Finbarr Timbers

4 accepted papers

2024

Reward-Respecting Subtasks for Model-Based Reinforcement Learning (Abstract Reprint)

Richard S. Sutton, Marlos C. Machado, G. Zacharias Holland, David Szepesvari, Finbarr Timbers, Brian Tanner +1
AAAI 2024technical

To achieve the ambitious goals of artificial intelligence, reinforcement learning must include planning with a model of the world that is abstract in state and time. Deep learning has made progress with state abstraction, but temporal abstraction has rarely been used, despite extensively developed t…

Cited by 0Source ↗PDF ↗Scholar ↗
2022

Approximate Exploitability: Learning a Best Response

Finbarr Timbers, Nolan Bard, Edward Lockhart, Marc Lanctot, Martin Schmid, Neil Burch +3
IJCAI 2022poster

Researchers have shown that neural networks are vulnerable to adversarial examples and subtle environment changes. The resulting errors can look like blunders to humans, eroding trust in these agents. In prior games research, agent evaluation often focused on the in-practice game outcomes. Such eva…

Cited by 0Source ↗PDF ↗Scholar ↗
2021

Solving Common-Payoff Games with Approximate Policy Iteration

Samuel Sokota, Edward Lockhart, Finbarr Timbers, Elnaz Davoodi, Ryan D'Orazio, Neil Burch +3
AAAI 2021technical

For artificially intelligent learning systems to have widespread applicability in real-world settings, it is important that they be able to operate decentrally. Unfortunately, decentralized control is difficult---computing even an epsilon-optimal joint policy is a NEXP complete problem. Nevertheless…

Cited by 19Source ↗PDF ↗code ↗Scholar ↗
2020

Fast computation of Nash Equilibria in Imperfect Information Games

Remi Munos, Julien Perolat, Jean-Baptiste Lespiau, Mark Rowland, Bart De Vylder, Marc Lanctot +7
ICML 2020poster

We introduce and analyze a class of algorithms, called Mirror Ascent against an Improved Opponent (MAIO), for computing Nash equilibria in two-player zero-sum games, both in normal form and in sequential form with imperfect information. These algorithms update the policy of each player with a mirror…

Cited by 12Source ↗PDF ↗Scholar ↗