Enhancing Multi-agent Coordination Through Common Operating Picture Integration
2023 Β· Peihong Yu, Bhoram Lee, Aswin Raghavan, et al.
Abstract
In multi-agent systems, agents possess only local observations of the environment. Communication between teammates becomes crucial for enhancing coordination. Past research has primarily focused on encoding local information into embedding messages which are unintelligible to humans. We find that using these messages in agent's policy learning leads to brittle policies when tested on out-of-distribution initial states. We present an approach to multi-agent coordination, where each agent is equipped with the capability to integrate its (history of) observations, actions and messages received into a Common Operating Picture (COP) and disseminate the COP. This process takes into account the dynamic nature of the environment and the shared mission. We conducted experiments in the StarCraft2 environment to validate our approach. Our results demonstrate the efficacy of COP integration, and show that COP-based training leads to robust policies compared to state-of-the-art Multi-Agent Reinforc
Authors
(none)
Tags
Stats
Related papers
- Coach-player Multi-agent Reinforcement Learning For Dynamic Team Composition (2021)0.00
- Communicating Plans, Not Percepts: Scalable Multi-agent Coordination With Embodied World Models (2025)0.00
- Promoting Coordination Through Policy Regularization In Multi-agent Deep Reinforcement Learning (2019)0.00
- Improving Coordination In Small-scale Multi-agent Deep Reinforcement Learning Through Memory-driven Communication (2019)12.25
- A Perspective On Multi-agent Communication For Information Fusion (2019)0.00
- Learning Multi-agent Coordination Through Connectivity-driven Communication (2020)8.60
- Signal Instructed Coordination In Cooperative Multi-agent Reinforcement Learning (2019)4.52
- Counterfactual Multi-agent Policy Gradients (2017)0.00