Co-Optimization of Environment and Policies for Decentralized Multi-Agent Navigation

• URL: http://arxiv.org/abs/2403.14583v1
• Date: Thu, 21 Mar 2024 17:37:43 GMT
• Title: Co-Optimization of Environment and Policies for Decentralized Multi-Agent Navigation
• Authors: Zhan Gao, Guang Yang, Amanda Prorok,
• Abstract summary: This work views the multi-agent system and its surrounding environment as a co-evolving system, where the behavior of one affects the other. We develop an algorithm that alternates between sub-objectives to search for an optimal of agent actions and obstacle configurations in the environment.
• Score: 14.533605727697775
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This work views the multi-agent system and its surrounding environment as a co-evolving system, where the behavior of one affects the other. The goal is to take both agent actions and environment configurations as decision variables, and optimize these two components in a coordinated manner to improve some measure of interest. Towards this end, we consider the problem of decentralized multi-agent navigation in cluttered environments. By introducing two sub-objectives of multi-agent navigation and environment optimization, we propose an $\textit{agent-environment co-optimization}$ problem and develop a $\textit{coordinated algorithm}$ that alternates between these sub-objectives to search for an optimal synthesis of agent actions and obstacle configurations in the environment; ultimately, improving the navigation performance. Due to the challenge of explicitly modeling the relation between agents, environment and performance, we leverage policy gradient to formulate a model-free learning mechanism within the coordinated framework. A formal convergence analysis shows that our coordinated algorithm tracks the local minimum trajectory of an associated time-varying non-convex optimization problem. Extensive numerical results corroborate theoretical findings and show the benefits of co-optimization over baselines. Interestingly, the results also indicate that optimized environment configurations are able to offer structural guidance that is key to de-conflicting agents in motion.

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