812 citations · 954 across the 15 of their papers we have counts for
8 papers · 1 filter
Coachable agents for interactive gameplay
Roberto Capobianco, Harm van Seijen, Nolan D. Bard +38
Reinforcement learning has proven to be a valuable tool in the creation of advanced AI and robotic systems, contributing to everything from game playing to robotics to foundation m…
The Partially Observable History Process
Dustin Morrill, Amy R. Greenwald, Michael Bowling
We introduce the partially observable history process (POHP) formalism for reinforcement learning. POHP centers around the actions and observations of a single agent and abstracts…
Learning to Be Cautious
Montaser Mohammedalamen, Dustin Morrill, Alexander Sieusahai +2
A key challenge in the field of reinforcement learning is to develop agents that behave cautiously in novel situations. It is generally impossible to anticipate all situations that…
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…
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…
Computing Approximate Equilibria in Sequential Adversarial Games by Exploitability Descent
Edward Lockhart, Marc Lanctot, Julien Pérolat +4
In this paper, we present exploitability descent, a new algorithm to compute approximate equilibria in two-player zero-sum extensive-form games with imperfect information, by direc…
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…
Solving Games with Functional Regret Estimation
Kevin Waugh, Dustin Morrill, J. Andrew Bagnell +1
We propose a novel online learning method for minimizing regret in large extensive-form games. The approach learns a function approximator online to estimate the regret for choosin…