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Cognition-Oriented Multiagent Reinforcement Learning
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AI
DOI：10.1109/TNNLS.2024.3502176.png)
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Abstract
En 中文
Inspired by psychological insights into individual behavior, we propose a novel cognition-oriented multiagent reinforcement learning (CORL) framework. CORL equips agents with two distinct types of cognition-situational and self-cognition-derived from local observations. To enhance the informativeness and precision of these cognition types, we introduce two information-theoretical regularizers: one to align situational cognition with the global state and the other to align self-cognition with each agent's identity for improved role differentiation and team coordination. In addition, the centralized training and decentralized execution framework is adopted to train the policy network. Our simulations demonstrate that CORL effectively harnesses local observations for enriched cooperation, leading to pronounced performance improvements, particularly in challenging tasks.
Keywords:
Cognition
Training
Reinforcement learning
Observability
Multi-agent systems
Data mining
Automation
Trajectory
Mutual information
Simulation
Centralized training with decentralized execution
cognition learning
multiagent reinforcement learning (MARL)
mutual information
Journal
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