On reinforcement learning for full-length game of starcraft
StarCraft II poses a grand challenge for reinforcement learning. The main difficulties include
huge state space, varying action space, long horizon, etc. In this paper, we investigate a set …
huge state space, varying action space, long horizon, etc. In this paper, we investigate a set …
Alphastar unplugged: Large-scale offline reinforcement learning
StarCraft II is one of the most challenging simulated reinforcement learning environments; it
is partially observable, stochastic, multi-agent, and mastering StarCraft II requires strategic …
is partially observable, stochastic, multi-agent, and mastering StarCraft II requires strategic …
Starcraft ii unplugged: Large scale offline reinforcement learning
StarCraft II is one of the most challenging reinforcement learning (RL) environments; it is
partially observable, stochastic, and multi-agent, and mastering StarCraft II requires strategic …
partially observable, stochastic, and multi-agent, and mastering StarCraft II requires strategic …
Tikick: Towards playing multi-agent football full games from single-agent demonstrations
Deep reinforcement learning (DRL) has achieved super-human performance on complex
video games (eg, StarCraft II and Dota II). However, current DRL systems still suffer from …
video games (eg, StarCraft II and Dota II). However, current DRL systems still suffer from …
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 …
Macro action selection with deep reinforcement learning in starcraft
S Xu, H Kuang, Z Zhi, R Hu, Y Liu, H Sun - Proceedings of the AAAI …, 2019 - aaai.org
StarCraft (SC) is one of the most popular and successful Real Time Strategy (RTS) games.
In recent years, SC is also widely accepted as a challenging testbed for AI research because …
In recent years, SC is also widely accepted as a challenging testbed for AI research because …
Relational deep reinforcement learning
We introduce an approach for deep reinforcement learning (RL) that improves upon the
efficiency, generalization capacity, and interpretability of conventional approaches through …
efficiency, generalization capacity, and interpretability of conventional approaches through …
Reward learning from human preferences and demonstrations in atari
To solve complex real-world problems with reinforcement learning, we cannot rely on
manually specified reward functions. Instead, we need humans to communicate an objective …
manually specified reward functions. Instead, we need humans to communicate an objective …
Beating the world's best at Super Smash Bros. with deep reinforcement learning
V Firoiu, WF Whitney, JB Tenenbaum - arXiv preprint arXiv:1702.06230, 2017 - arxiv.org
There has been a recent explosion in the capabilities of game-playing artificial intelligence.
Many classes of RL tasks, from Atari games to motor control to board games, are now …
Many classes of RL tasks, from Atari games to motor control to board games, are now …
Efficient Reinforcement Learning for StarCraft by Abstract Forward Models and Transfer Learning
Injecting human knowledge is an effective way to accelerate reinforcement learning (RL).
However, these methods are underexplored. This article presents our discovery that an …
However, these methods are underexplored. This article presents our discovery that an …