Multi-agent deep reinforcement learning using attentive graph neural architectures for real-time strategy games
In real-time strategy (RTS) game artificial intelligence research, various multi-agent deep
reinforcement learning (MADRL) algorithms are widely and actively used nowadays. Most of …
reinforcement learning (MADRL) algorithms are widely and actively used nowadays. Most of …
Maidrl: Semi-centralized multi-agent reinforcement learning using agent influence
In recent years, reinforcement learning algorithms have been used in the field of multi-agent
systems to help the agents with interactions and cooperation on a variety of tasks …
systems to help the agents with interactions and cooperation on a variety of tasks …
Learning Heterogeneous Strategies via Graph-based Multi-agent Reinforcement Learning
In a mixed cooperative-competitive environment, each agent needs to learn heterogeneous
strategies. The complex game relationship between heterogeneous agents causes …
strategies. The complex game relationship between heterogeneous agents causes …
Graph convolutional value decomposition in multi-agent reinforcement learning
N Naderializadeh, FH Hung, S Soleyman… - arXiv preprint arXiv …, 2020 - arxiv.org
We propose a novel framework for value function factorization in multi-agent deep
reinforcement learning (MARL) using graph neural networks (GNNs). In particular, we …
reinforcement learning (MARL) using graph neural networks (GNNs). In particular, we …
Deep multi-agent reinforcement learning with discrete-continuous hybrid action spaces
Deep Reinforcement Learning (DRL) has been applied to address a variety of cooperative
multi-agent problems with either discrete action spaces or continuous action spaces …
multi-agent problems with either discrete action spaces or continuous action spaces …
Explainable and adaptable augmentation in knowledge attention network for multi-agent deep reinforcement learning systems
The scale of modem Artificial Intelligence systems has been growing and entering more
research territories by incorporating Deep Learning (DL) and Deep Reinforcement Learning …
research territories by incorporating Deep Learning (DL) and Deep Reinforcement Learning …
Ghgc: Goal-based hierarchical group communication in multi-agent reinforcement learning
In large-scale multi-agent systems, the existence of a large number of agents with different
target tasks and connected by complex game relationships causes great difficulty for policy …
target tasks and connected by complex game relationships causes great difficulty for policy …
Aggregation transfer learning for multi-agent reinforcement learning
D Xu, P Qiao, Y Dou - … International Conference on Big Data & …, 2021 - ieeexplore.ieee.org
Multi-agent reinforcement learning is currently mainly used in many real-time strategy
games. For example, StarCraft, UAV combat. Multi-agent reinforcement learning algorithms …
games. For example, StarCraft, UAV combat. Multi-agent reinforcement learning algorithms …
A survey of deep reinforcement learning in video games
Deep reinforcement learning (DRL) has made great achievements since proposed.
Generally, DRL agents receive high-dimensional inputs at each step, and make actions …
Generally, DRL agents receive high-dimensional inputs at each step, and make actions …
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 …