812 citations · 825 across the 8 of their papers we have counts for
6 papers · 1 filter
Solving Common-Payoff Games with Approximate Policy Iteration
Samuel Sokota, Edward Lockhart, Finbarr Timbers +6
For artificially intelligent learning systems to have widespread applicability in real-world settings, it is important that they be able to operate decentrally. Unfortunately, dece…
The Advantage Regret-Matching Actor-Critic
Audrūnas Gruslys, Marc Lanctot, Rémi Munos +10
Regret minimization has played a key role in online learning, equilibrium computation in games, and reinforcement learning (RL). In this paper, we describe a general model-free RL…
Marginal Utility for Planning in Continuous or Large Discrete Action Spaces
Zaheen Farraz Ahmad, Levi H. S. Lelis, Michael Bowling
Sample-based planning is a powerful family of algorithms for generating intelligent behavior from a model of the environment. Generating good candidate actions is critical to the s…
Alternative Function Approximation Parameterizations for Solving Games: An Analysis of -Regression Counterfactual Regret Minimization
Ryan D'Orazio, Dustin Morrill, James R. Wright +1
Function approximation is a powerful approach for structuring large decision problems that has facilitated great achievements in the areas of reinforcement learning and game playin…
The Effect of Planning Shape on Dyna-style Planning in High-dimensional State Spaces
G. Zacharias Holland, Erin J. Talvitie, Michael Bowling
Dyna is a fundamental approach to model-based reinforcement learning (MBRL) that interleaves planning, acting, and learning in an online setting. In the most typical application of…
DeepStack: Expert-Level Artificial Intelligence in No-Limit Poker
Matej Moravčík, Martin Schmid, Neil Burch +7
Artificial intelligence has seen several breakthroughs in recent years, with games often serving as milestones. A common feature of these games is that players have perfect informa…