Learning Transferable Cooperative Behavior in Multi-Agent Teams (1906.01202v1)

Abstract: While multi-agent interactions can be naturally modeled as a graph, the environment has traditionally been considered as a black box. We propose to create a shared agent-entity graph, where agents and environmental entities form vertices, and edges exist between the vertices which can communicate with each other. Agents learn to cooperate by exchanging messages along the edges of this graph. Our proposed multi-agent reinforcement learning framework is invariant to the number of agents or entities present in the system as well as permutation invariance, both of which are desirable properties for any multi-agent system representation. We present state-of-the-art results on coverage, formation and line control tasks for multi-agent teams in a fully decentralized framework and further show that the learned policies quickly transfer to scenarios with different team sizes along with strong zero-shot generalization performance. This is an important step towards developing multi-agent teams which can be realistically deployed in the real world without assuming complete prior knowledge or instantaneous communication at unbounded distances.

• The paper introduces a novel shared agent-entity graph model that overcomes non-stationarity and scales to various team sizes through effective inter-agent communication.
• It employs Graph Neural Networks and curriculum learning to ensure permutation invariance and gradual mastery of complex coordination tasks.
• The approach achieves strong zero-shot generalization and outperforms existing methods in coverage, formation, and line control tasks.

Learning Transferable Cooperative Behavior in Multi-Agent Teams

The paper "Learning Transferable Cooperative Behavior in Multi-Agent Teams" introduces an innovative approach for modeling environments in Multi-Agent Reinforcement Learning (MARL) using a shared agent-entity graph. The research addresses core challenges in MARL, notably the non-stationarity of environments, the complexity of joint action and state spaces, and issues relating to partial observability and limited communication. By exploiting the environmental structure, this work sets a new benchmark for cooperative behaviors in multi-agent systems across coverage, formation, and line control tasks.

Key Contributions

1. Shared Agent-Entity Graph: The researchers propose modeling environments as a graph where both agents and environmental entities form vertices. Edges connect vertices that can communicate, facilitating cooperative behavior through message exchange. This model leverages Graph Neural Networks to maintain permutation invariance and an ability to scale with varying numbers of agents and entities.
2. Decentralized Framework: The paper reports state-of-the-art results in decentralized scenarios, demonstrating that teams can achieve tasks such as coverage, formation, and line control in a scalable manner without relying on a central orchestrator or full observability.
3. Generalization and Transferability: A significant insight from this paper is the model's ability to transfer learned policies across different team sizes with remarkable performance, even achieving strong zero-shot generalization. The invariant nature of their architecture supports the adaptability of agents to newly configured environments without retraining from scratch.
4. Communication Mechanisms: The paper effectively incorporates communication through inter-agent message passing combined with attention mechanisms, allowing agents to dynamically adapt their focus to relevant inputs from neighboring agents. This is crucial for complex coordination tasks, ensuring adaptability even in constrained communication bandwidths.
5. Curriculum Learning Implementation: The adaptable nature of the model also enables curriculum learning, where agents iteratively extend their learned behavior in progressively challenging environments. This method allows agents to acquire increasingly complex cooperative strategies effectively, a concept explored through various numbers of agents in swarm tasks.

Numerical Results

The model demonstrates demonstrable superiorities over existing methods, including Q-Mix, VDN, IQL, COMA, and MADDPG, particularly in scenarios of partial observability without centralized critics. Notably, the architecture efficiently handles increases in agent numbers and shows resilience under restricted communication scenarios.

Implications and Future Work

From a theoretical perspective, this research advances the understanding of how structural biases in modeling environments can lead to robust learning algorithms in MARL. Practical applications envision deploying such adaptable agent teams in real-world settings like autonomous driving, robotic swarms, or resource management systems where environmental dynamics are complex and possibly evolving.

The paper hints at potential extensions including adversarial settings where opposing agents may exist. Developing strategies for curriculum learning in scenarios involving adversaries promises to be a compelling avenue. Exploring discrete and dynamic environments beyond static entities could also introduce new challenges and frontiers.

Overall, the proposed model opens pathways for practical implementations in dynamic, real-world multi-agent systems, supporting adaptable intelligence that is crucial for autonomy in complex scenarios.