Search Results for author:
Found 14 papers, 3 papers with code
Learning to Beat ByteRL: Exploitability of Collectible Card Game Agents
While Poker, as a family of games, has been studied extensively in the last decades, collectible card games have seen relatively little attention.
Learning not to Regret
As these similar games feature similar equilibra, we investigate a way to accelerate equilibrium finding on such a distribution.
Student of Games: A unified learning algorithm for both perfect and imperfect information games
Games have a long history as benchmarks for progress in artificial intelligence.
Search in Imperfect Information Games
From the very dawn of the field, search with value functions was a fundamental concept of computer games research.
Solving Common-Payoff Games with Approximate Policy Iteration
While this choice precludes CAPI from scaling to games as large as Hanabi, empirical results demonstrate that, on the games to which CAPI does scale, it is capable of discovering optimal joint policies even when other modern multi-agent reinforcement learning algorithms are unable to do so.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
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.
Approximate exploitability: Learning a best response in large games
In prior games research, agent evaluation often focused on the in-practice game outcomes.
Low-Variance and Zero-Variance Baselines for Extensive-Form Games
In this paper, we extend recent work that uses baseline estimates to reduce this variance.
Rethinking Formal Models of Partially Observable Multiagent Decision Making
A second issue is that while EFGs have recently seen significant algorithmic progress, their classical formalization is unsuitable for efficient presentation of the underlying ideas, such as those around decomposition.
Variance Reduction in Monte Carlo Counterfactual Regret Minimization (VR-MCCFR) for Extensive Form Games using Baselines
The new formulation allows estimates to be bootstrapped from other estimates within the same episode, propagating the benefits of baselines along the sampled trajectory; the estimates remain unbiased even when bootstrapping from other estimates.
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.
AIVAT: A New Variance Reduction Technique for Agent Evaluation in Imperfect Information Games
Evaluating agent performance when outcomes are stochastic and agents use randomized strategies can be challenging when there is limited data available.
Text Understanding with the Attention Sum Reader Network
Several large cloze-style context-question-answer datasets have been introduced recently: the CNN and Daily Mail news data and the Children's Book Test.
Ranked #5 on
Open-Domain Question Answering
on SearchQA
(Unigram Acc metric)
Machine Reading Comprehension
Open-Domain Question Answering
Improved Deep Learning Baselines for Ubuntu Corpus Dialogs
The ensemble further improves the performance and it achieves a state-of-the-art result for the next utterance ranking on this dataset.
