Cooperative and competitive multi-agent systems: From optimization to games
Multi-agent systems can solve scientific issues related to complex systems that are difficult or
impossible for a single agent to solve through mutual collaboration and cooperation …
impossible for a single agent to solve through mutual collaboration and cooperation …
[HTML][HTML] Distributed deep reinforcement learning: A survey and a multi-player multi-agent learning toolbox
With the breakthrough of AlphaGo, deep reinforcement learning has become a recognized
technique for solving sequential decision-making problems. Despite its reputation, data …
technique for solving sequential decision-making problems. Despite its reputation, data …
[HTML][HTML] Ai in human-computer gaming: Techniques, challenges and opportunities
With the breakthrough of AlphaGo, human-computer gaming AI has ushered in a big
explosion, attracting more and more researchers all over the world. As a recognized …
explosion, attracting more and more researchers all over the world. As a recognized …
A robust and opponent-aware league training method for StarCraft II
R Huang, X Wu, H Yu, Z Fan, H Fu… - Advances in Neural …, 2024 - proceedings.neurips.cc
It is extremely difficult to train a superhuman Artificial Intelligence (AI) for games of similar
size to StarCraft II. AlphaStar is the first AI that beat human professionals in the full game of …
size to StarCraft II. AlphaStar is the first AI that beat human professionals in the full game of …
On games and simulators as a platform for development of artificial intelligence for command and control
Games and simulators can be a valuable platform to execute complex multi-agent,
multiplayer, imperfect information scenarios with significant parallels to military applications …
multiplayer, imperfect information scenarios with significant parallels to military applications …
On efficient reinforcement learning for full-length game of starcraft ii
StarCraft II (SC2) poses a grand challenge for reinforcement learning (RL), of which the
main difficulties include huge state space, varying action space, and a long time horizon. In …
main difficulties include huge state space, varying action space, and a long time horizon. In …
Two-stream fused fuzzy deep neural network for multiagent learning
In multiagent reinforcement learning (RL), multilayer fully connected neural network is used
for value function approximation, which solves large-scale or continuous space problems …
for value function approximation, which solves large-scale or continuous space problems …
Stackelberg policy gradient: Evaluating the performance of leaders and followers
QL Vu, Z Alumbaugh, R Ching, Q Ding… - ICLR 2022 Workshop …, 2022 - openreview.net
Hierarchical order of play is an important concept for reinforcement learning to understand
better the decisions made by strategic agents in a shared environment. In this paper, we …
better the decisions made by strategic agents in a shared environment. In this paper, we …
[HTML][HTML] SC2EGSet: StarCraft II esport replay and game-state dataset
As a relatively new form of sport, esports offers unparalleled data availability. Our work aims
to open esports to a broader scientific community by supplying raw and pre-processed files …
to open esports to a broader scientific community by supplying raw and pre-processed files …
Deep learning applications in games: a survey from a data perspective
This paper presents a comprehensive review of deep learning applications in the video
game industry, focusing on how these techniques can be utilized in game development …
game industry, focusing on how these techniques can be utilized in game development …