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Hierarchical and non-hierarchical multi-agent interactions based on unity reinforcement learning

conference contribution
posted on 2023-05-23, 14:36 authored by Cao, Z, Wong, K, Quan BaiQuan Bai, Lin, C-T

The open-source Unity platform, where agents can be trained using hierarchical or non-hierarchical reinforcement learning, supports the use of games and simulations as environments for multipleagent interactions. In this demonstration, we present hierarchical and non-hierarchical multi-agent interactions based on Unity reinforcement learning, specifically, hierarchical reinforcement learning that sets different levels of agent’s observations to achieve the goal. We created four multi-agent scenarios in the Unity environment, namely, Crawler, Tennis, Banana Collector, and Soccer, to test the interaction performances of hierarchical and nonhierarchical reinforcement learning. The simulation-interaction performances show that hierarchical reinforcement learning can be applied to multi-agent environments and can compete with agents trained via non-hierarchical reinforcement learning.

The demonstration video can be viewed at the following link: https://youtu.be/YQYQwLPXaL4

History

Publication title

Proceedings of the 19th International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2020)

Editors

B An, N Yorke-Smith, A El Fallah Seghrouchni, and G Sukthankar

Pagination

2095-2097

Department/School

School of Information and Communication Technology

Publisher

International Foundation for Autonomous Agents and Multiagent Systems

Place of publication

United States

Event title

19th International Conference on Autonomous Agents and Multiagent Systems 2020

Event Venue

University of Auckland (virtual/online)

Date of Event (Start Date)

2020-05-09

Date of Event (End Date)

2020-05-13

Rights statement

Copyright 2020 International Foundation for Autonomous Agents and Multiagent Systems

Repository Status

  • Restricted

Socio-economic Objectives

Intelligence, surveillance and space

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