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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 …
Tizero: Mastering multi-agent football with curriculum learning and self-play
Multi-agent football poses an unsolved challenge in AI research. Existing work has focused
on tackling simplified scenarios of the game, or else leveraging expert demonstrations. In …
on tackling simplified scenarios of the game, or else leveraging expert demonstrations. In …
Hokoff: Real game dataset from honor of kings and its offline reinforcement learning benchmarks
Abstract The advancement of Offline Reinforcement Learning (RL) and Offline Multi-Agent
Reinforcement Learning (MARL) critically depends on the availability of high-quality, pre …
Reinforcement Learning (MARL) critically depends on the availability of high-quality, pre …
Towards effective and interpretable human-agent collaboration in moba games: A communication perspective
MOBA games, eg, Dota2 and Honor of Kings, have been actively used as the testbed for the
recent AI research on games, and various AI systems have been developed at the human …
recent AI research on games, and various AI systems have been developed at the human …
Himacmic: Hierarchical multi-agent deep reinforcement learning with dynamic asynchronous macro strategy
Multi-agent deep reinforcement learning (MADRL) has been widely used in many scenarios
such as robotics and game AI. However, existing methods mainly focus on the optimization …
such as robotics and game AI. However, existing methods mainly focus on the optimization …
Enhancing human experience in human-agent collaboration: A human-centered modeling approach based on positive human gain
Existing game AI research mainly focuses on enhancing agents' abilities to win games, but
this does not inherently make humans have a better experience when collaborating with …
this does not inherently make humans have a better experience when collaborating with …
Enhancing AI-Bot Strength and Strategy Diversity in Adversarial Games: A Novel Deep Reinforcement Learning Framework
Deep reinforcement learning (DRL) has emerged as a leading technique for designing AI-
bots in the gaming industry. However, practical implementation of DRL-trained bots often …
bots in the gaming industry. However, practical implementation of DRL-trained bots often …
Representing the Information of Multiplayer Online Battle Arena (MOBA) Video Games Using Convolutional Accordion Auto-Encoder (A 2 E) Enhanced by Attention …
In this paper, we propose a representation of the visual information about Multiplayer Online
Battle Arena (MOBA) video games using an adapted unsupervised deep learning …
Battle Arena (MOBA) video games using an adapted unsupervised deep learning …
A Goal-Conditioned Reinforcement Learning Algorithm with Environment Modeling
Goal-conditioned Reinforcement Learning (GcRL) has achieved remarkable success in
navigating towards goals in recent years. However, learning efficiency and generalization …
navigating towards goals in recent years. However, learning efficiency and generalization …
Multi-Agent Multi-Game Entity Transformer
Building large-scale generalist pre-trained models for many tasks is becoming an emerging
and potential direction in reinforcement learning (RL). Research such as Gato and Multi …
and potential direction in reinforcement learning (RL). Research such as Gato and Multi …