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Title: End-to-end deep reinforcement learning for multi-agent collaborative exploration
Authors: Chen, Zichen
Subagdja, Bhuditama
Tan, Ah-Hwee
Keywords: Engineering::Computer science and engineering::Computing methodologies::Artificial intelligence
Issue Date: 2019
Source: Chen, Z., Subagdja, B. & Tan, A. (2019). End-to-end deep reinforcement learning for multi-agent collaborative exploration. 2019 IEEE International Conference on Agents (ICA), 99-102.
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.
ISBN: 9781728140261
DOI: 10.1109/AGENTS.2019.8929192
Rights: © 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The published version is available at:
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:EEE Conference Papers

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