Multi-agent reinforcement learning: A selective overview of theories and algorithms
Recent years have witnessed significant advances in reinforcement learning (RL), which
has registered tremendous success in solving various sequential decision-making problems …
has registered tremendous success in solving various sequential decision-making problems …
Deep reinforcement learning from self-play in imperfect-information games
J Heinrich, D Silver - arXiv preprint arXiv:1603.01121, 2016 - arxiv.org
Many real-world applications can be described as large-scale games of imperfect
information. To deal with these challenging domains, prior work has focused on computing …
information. To deal with these challenging domains, prior work has focused on computing …
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. Approaches
using search and learning produced strong performance across many perfect information …
using search and learning produced strong performance across many perfect information …
Fictitious self-play in extensive-form games
Fictitious play is a popular game-theoretic model of learning in games. However, it has
received little attention in practical applications to large problems. This paper introduces two …
received little attention in practical applications to large problems. This paper introduces two …
Strategical argumentative agent for human persuasion
A Rosenfeld, S Kraus - ECAI 2016, 2016 - ebooks.iospress.nl
Automated agents should be able to persuade people in the same way people persuade
each other-via dialogs. Today, automated persuasion modeling and research use unnatural …
each other-via dialogs. Today, automated persuasion modeling and research use unnatural …
[PDF][PDF] DeltaDou: Expert-level Doudizhu AI through Self-play.
Q Jiang, K Li, B Du, H Chen, H Fang - IJCAI, 2019 - ijcai.org
Artificial Intelligence has seen several breakthroughs in two-player perfect information
game. Nevertheless, Doudizhu, a three-player imperfect information game, is still quite …
game. Nevertheless, Doudizhu, a three-player imperfect information game, is still quite …
Reinforcement learning and its connections with neuroscience and psychology
Reinforcement learning methods have recently been very successful at performing complex
sequential tasks like playing Atari games, Go and Poker. These algorithms have …
sequential tasks like playing Atari games, Go and Poker. These algorithms have …
A Monte Carlo Neural Fictitious Self-Play approach to approximate Nash Equilibrium in imperfect-information dynamic games
L Zhang, Y Chen, W Wang, Z Han, S Li, Z Pan… - Frontiers of Computer …, 2021 - Springer
Solving the optimization problem to approach a Nash Equilibrium point plays an important
role in imperfect information games, eg, StarCraft and poker. Neural Fictitious Self-Play …
role in imperfect information games, eg, StarCraft and poker. Neural Fictitious Self-Play …
Enhancing monte carlo tree search for playing hearthstone
JSB Choe, JK Kim - 2019 IEEE conference on games (CoG), 2019 - ieeexplore.ieee.org
Hearthstone is a popular online collectible card game (CCG). Hearthstone imposes
interesting challenges in developing a search algorithm for the game AI. As a CCG, it has a …
interesting challenges in developing a search algorithm for the game AI. As a CCG, it has a …