Publication Type
Conference Proceeding Article
Version
acceptedVersion
Publication Date
10-2019
Abstract
Exploring an unknown environment by multiple autonomous robots is a major challenge in robotics domains. As multiple robots are assigned to explore different locations, they may interfere each other making the overall tasks less efficient. In this paper, we present a new model called CNN-based Multi-agent Proximal Policy Optimization (CMAPPO) to multi-agent exploration wherein the agents learn the effective strategy to allocate and explore the environment using a new deep reinforcement learning architecture. The model combines convolutional neural network to process multi-channel visual inputs, curriculum-based learning, and PPO algorithm for motivation based reinforcement learning. Evaluations show that the proposed method can learn more efficient strategy for multiple agents to explore the environment than the conventional frontier-based method.
Keywords
Deep learning, Multi-agent exploration, Reinforcement Learning
Discipline
Artificial Intelligence and Robotics | Databases and Information Systems
Research Areas
Data Science and Engineering
Publication
Proceedings of 2019 IEEE International Conference on Agents, ICA 2019, Jinan, China, October 18-21
First Page
99
Last Page
102
ISBN
9781728140261
Identifier
10.1109/AGENTS.2019.8929192
Publisher
Institute of Electrical and Electronics Engineers Inc.
City or Country
Jinan
Citation
1
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.