Investigating Relational State Abstraction in Collaborative MARL

• URL: http://arxiv.org/abs/2412.15388v1
• Date: Thu, 19 Dec 2024 20:34:00 GMT
• Title: Investigating Relational State Abstraction in Collaborative MARL
• Authors: Sharlin Utke, Jeremie Houssineau, Giovanni Montana,
• Abstract summary: This paper explores the impact of state abstraction on sample efficiency and performance in collaborative Multi-Agent Reinforcement Learning. The proposed abstraction is based on spatial relationships in environments where direct communication between agents is not allowed. We introduce MARC (Multi-Agent Critic), incorporating inductive biases by transforming state into a spatial graph and processing it through a relational graph neural network.
• Score: 5.052293146674794
• License:
• Abstract: This paper explores the impact of relational state abstraction on sample efficiency and performance in collaborative Multi-Agent Reinforcement Learning. The proposed abstraction is based on spatial relationships in environments where direct communication between agents is not allowed, leveraging the ubiquity of spatial reasoning in real-world multi-agent scenarios. We introduce MARC (Multi-Agent Relational Critic), a simple yet effective critic architecture incorporating spatial relational inductive biases by transforming the state into a spatial graph and processing it through a relational graph neural network. The performance of MARC is evaluated across six collaborative tasks, including a novel environment with heterogeneous agents. We conduct a comprehensive empirical analysis, comparing MARC against state-of-the-art MARL baselines, demonstrating improvements in both sample efficiency and asymptotic performance, as well as its potential for generalization. Our findings suggest that a minimal integration of spatial relational inductive biases as abstraction can yield substantial benefits without requiring complex designs or task-specific engineering. This work provides insights into the potential of relational state abstraction to address sample efficiency, a key challenge in MARL, offering a promising direction for developing more efficient algorithms in spatially complex environments.

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