Battlefield Environment Design for Multi-agent Reinforcement Learning

被引：2

作者：

Do, Seungwon ^{[1
]}

Baek, Jaeuk ^{[1
]}

Jun, Sungwoo ^{[1
]}

Lee, Changeun ^{[1
]}

机构：

[1] ETRI, Intelligent Convergence Res Lab, Daejeon 34129, South Korea

来源：

2022 IEEE INTERNATIONAL CONFERENCE ON BIG DATA AND SMART COMPUTING (IEEE BIGCOMP 2022) | 2022年

关键词：

Reinforcement learning; multi-agent reinforcement learning; environment; battlefield; PLATFORM;

D O I：

10.1109/BigComp54360.2022.00069

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

In reinforcement learning, an agent interacts with an environment for learning its policy. Designing the environment is an important part of training the agent because the change of the environment can affect the agent's policy. In this paper, we introduce the new battlefield environment for multi-agent reinforcement learning. In our environment, two groups of agents compete for their contrasting goals with limited information. As a result, we define the map of the environment and design the state, action, reward, and transition of each agent.

引用

页码：318 / 319

页数：2

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