Search Results for author:
Found 15 papers, 6 papers with code
Composing Efficient, Robust Tests for Policy Selection
Empirical results demonstrate that RPOSST finds a small set of test cases that identify high quality policies in a toy one-shot game, poker datasets, and a high-fidelity racing simulator.
Interpolating Between Softmax Policy Gradient and Neural Replicator Dynamics with Capped Implicit Exploration
Neural replicator dynamics (NeuRD) is an alternative to the foundational softmax policy gradient (SPG) algorithm motivated by online learning and evolutionary game theory.
Efficient Deviation Types and Learning for Hindsight Rationality in Extensive-Form Games: Corrections
Hindsight rationality is an approach to playing general-sum games that prescribes no-regret learning dynamics for individual agents with respect to a set of deviations, and further describes jointly rational behavior among multiple agents with mediated equilibria.
The Partially Observable History Process
We introduce the partially observable history process (POHP) formalism for reinforcement learning.
Learning to Be Cautious
An agent that could learn to be cautious would overcome this challenge by discovering for itself when and how to behave cautiously.
Efficient Deviation Types and Learning for Hindsight Rationality in Extensive-Form Games
Hindsight rationality is an approach to playing general-sum games that prescribes no-regret learning dynamics for individual agents with respect to a set of deviations, and further describes jointly rational behavior among multiple agents with mediated equilibria.
Hindsight and Sequential Rationality of Correlated Play
This approach also leads to a game-theoretic analysis, but in the correlated play that arises from joint learning dynamics rather than factored agent behavior at equilibrium.
The Advantage Regret-Matching Actor-Critic
In this paper, we describe a general model-free RL method for no-regret learning based on repeated reconsideration of past behavior.
Alternative Function Approximation Parameterizations for Solving Games: An Analysis of $f$-Regression Counterfactual Regret Minimization
In contrast, the more conventional softmax parameterization is standard in the field of reinforcement learning and yields a regret bound with a better dependence on the number of actions.
Bounds for Approximate Regret-Matching Algorithms
A common approach to incorporating function approximation is to learn the inputs needed for a regret minimizing algorithm, allowing for generalization across many regret minimization problems.
OpenSpiel: A Framework for Reinforcement Learning in Games
OpenSpiel is a collection of environments and algorithms for research in general reinforcement learning and search/planning in games.
Neural Replicator Dynamics
Policy gradient and actor-critic algorithms form the basis of many commonly used training techniques in deep reinforcement learning.
Computing Approximate Equilibria in Sequential Adversarial Games by Exploitability Descent
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 direct policy optimization against worst-case opponents.
DeepStack: Expert-Level Artificial Intelligence in No-Limit Poker
Poker is the quintessential game of imperfect information, and a longstanding challenge problem in artificial intelligence.
Solving Games with Functional Regret Estimation
We propose a novel online learning method for minimizing regret in large extensive-form games.
