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Updated: May 24, 2025

Assessment of Social Cognition in Non-human Primates Using a Network of Computerized Automated Learning Device ALDM Test Systems
Published on: May 5, 2015
Cognition-Oriented Multiagent Reinforcement Learning
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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.
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