Heterogeneous Embodied Multi-Agent Collaboration
- URL: http://arxiv.org/abs/2307.13957v2
- Date: Thu, 27 Jul 2023 00:53:11 GMT
- Title: Heterogeneous Embodied Multi-Agent Collaboration
- Authors: Xinzhu Liu, Di Guo, Huaping Liu
- Abstract summary: Heterogeneous multi-agent tasks are common in real-world scenarios.
We propose the heterogeneous multi-agent tidying-up task, in which multiple heterogeneous agents collaborate to detect misplaced objects and place them in reasonable locations.
We propose the hierarchical decision model based on misplaced object detection, reasonable receptacle prediction, as well as the handshake-based group communication mechanism.
- Score: 21.364827833498254
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Multi-agent embodied tasks have recently been studied in complex indoor
visual environments. Collaboration among multiple agents can improve work
efficiency and has significant practical value. However, most of the existing
research focuses on homogeneous multi-agent tasks. Compared with homogeneous
agents, heterogeneous agents can leverage their different capabilities to
allocate corresponding sub-tasks and cooperate to complete complex tasks.
Heterogeneous multi-agent tasks are common in real-world scenarios, and the
collaboration strategy among heterogeneous agents is a challenging and
important problem to be solved. To study collaboration among heterogeneous
agents, we propose the heterogeneous multi-agent tidying-up task, in which
multiple heterogeneous agents with different capabilities collaborate with each
other to detect misplaced objects and place them in reasonable locations. This
is a demanding task since it requires agents to make the best use of their
different capabilities to conduct reasonable task planning and complete the
whole task. To solve this task, we build a heterogeneous multi-agent tidying-up
benchmark dataset in a large number of houses with multiple rooms based on
ProcTHOR-10K. We propose the hierarchical decision model based on misplaced
object detection, reasonable receptacle prediction, as well as the
handshake-based group communication mechanism. Extensive experiments are
conducted to demonstrate the effectiveness of the proposed model. The project's
website and videos of experiments can be found at https://hetercol.github.io/.
Related papers
- QTypeMix: Enhancing Multi-Agent Cooperative Strategies through Heterogeneous and Homogeneous Value Decomposition [11.170571181947274]
We propose QTypeMix, which divides the value decomposition process into homogeneous and heterogeneous stages.
The results of testing the proposed method on 14 maps from SMAC and SMACv2 show that QTypeMix achieves state-of-the-art performance in tasks of varying difficulty.
arXiv Detail & Related papers (2024-08-12T12:27:58Z) - Scaling Large-Language-Model-based Multi-Agent Collaboration [75.5241464256688]
Pioneering advancements in large language model-powered agents have underscored the design pattern of multi-agent collaboration.
Inspired by the neural scaling law, this study investigates whether a similar principle applies to increasing agents in multi-agent collaboration.
arXiv Detail & Related papers (2024-06-11T11:02:04Z) - Multi-Agent Consensus Seeking via Large Language Models [6.922356864800498]
Multi-agent systems driven by large language models (LLMs) have shown promising abilities for solving complex tasks in a collaborative manner.
This work considers a fundamental problem in multi-agent collaboration: consensus seeking.
arXiv Detail & Related papers (2023-10-31T03:37:11Z) - AutoAgents: A Framework for Automatic Agent Generation [27.74332323317923]
AutoAgents is an innovative framework that adaptively generates and coordinates multiple specialized agents to build an AI team according to different tasks.
Our experiments on various benchmarks demonstrate that AutoAgents generates more coherent and accurate solutions than the existing multi-agent methods.
arXiv Detail & Related papers (2023-09-29T14:46:30Z) - AgentVerse: Facilitating Multi-Agent Collaboration and Exploring
Emergent Behaviors [93.38830440346783]
We propose a multi-agent framework framework that can collaboratively adjust its composition as a greater-than-the-sum-of-its-parts system.
Our experiments demonstrate that framework framework can effectively deploy multi-agent groups that outperform a single agent.
In view of these behaviors, we discuss some possible strategies to leverage positive ones and mitigate negative ones for improving the collaborative potential of multi-agent groups.
arXiv Detail & Related papers (2023-08-21T16:47:11Z) - Multi-agent Deep Covering Skill Discovery [50.812414209206054]
We propose Multi-agent Deep Covering Option Discovery, which constructs the multi-agent options through minimizing the expected cover time of the multiple agents' joint state space.
Also, we propose a novel framework to adopt the multi-agent options in the MARL process.
We show that the proposed algorithm can effectively capture the agent interactions with the attention mechanism, successfully identify multi-agent options, and significantly outperforms prior works using single-agent options or no options.
arXiv Detail & Related papers (2022-10-07T00:40:59Z) - Policy Diagnosis via Measuring Role Diversity in Cooperative Multi-agent
RL [107.58821842920393]
We quantify the agent's behavior difference and build its relationship with the policy performance via bf Role Diversity
We find that the error bound in MARL can be decomposed into three parts that have a strong relation to the role diversity.
The decomposed factors can significantly impact policy optimization on three popular directions.
arXiv Detail & Related papers (2022-06-01T04:58:52Z) - LDSA: Learning Dynamic Subtask Assignment in Cooperative Multi-Agent
Reinforcement Learning [122.47938710284784]
We propose a novel framework for learning dynamic subtask assignment (LDSA) in cooperative MARL.
To reasonably assign agents to different subtasks, we propose an ability-based subtask selection strategy.
We show that LDSA learns reasonable and effective subtask assignment for better collaboration.
arXiv Detail & Related papers (2022-05-05T10:46:16Z) - Multi-Agent Embodied Visual Semantic Navigation with Scene Prior
Knowledge [42.37872230561632]
In visual semantic navigation, the robot navigates to a target object with egocentric visual observations and the class label of the target is given.
Most of the existing models are only effective for single-agent navigation, and a single agent has low efficiency and poor fault tolerance when completing more complicated tasks.
We propose the multi-agent visual semantic navigation, in which multiple agents collaborate with others to find multiple target objects.
arXiv Detail & Related papers (2021-09-20T13:31:03Z) - Multi-Agent Imitation Learning with Copulas [102.27052968901894]
Multi-agent imitation learning aims to train multiple agents to perform tasks from demonstrations by learning a mapping between observations and actions.
In this paper, we propose to use copula, a powerful statistical tool for capturing dependence among random variables, to explicitly model the correlation and coordination in multi-agent systems.
Our proposed model is able to separately learn marginals that capture the local behavioral patterns of each individual agent, as well as a copula function that solely and fully captures the dependence structure among agents.
arXiv Detail & Related papers (2021-07-10T03:49:41Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.