Adaptive Switching Between Self-Reinforcement and Mutual Reinforcement in Multi-Agent Systems: A Dynamic Framework for Individual and Collective Intelligence Optimization

Abstract

Multi-agent systems can improve their performance through individual learning and inter-agent cooperation. However, relying exclusively on individual reinforcement may limit the exploitation of complementary knowledge, whereas persistent mutual reinforcement may introduce communication overhead, correlated errors, excessive behavioral conformity, and dependence on potentially unreliable agents. These limitations motivate a dynamic framework in which agents adaptively switch between self-reinforcement and mutual reinforcement according to task conditions, learning progress, uncertainty, interaction quality, and resource constraints. This paper proposes Adaptive Switching between Self-Reinforcement and Mutual Reinforcement (AS-SMR), a general framework for multi-agent systems. Self-reinforcement enables an agent to improve its policy through individual experience, self-evaluation, and locally available feedback. Mutual reinforcement enables agents to improve their policies through reciprocal feedback, cross-agent evaluation, experience exchange, and mutually informative learning signals. A context-sensitive controller estimates the relative utility of these mechanisms and selects individual reinforcement, mutual reinforcement, or a hybrid mode. The framework further incorporates reinforcement credibility, interaction costs, diversity preservation, and feedback-based switching to prevent unnecessary collaboration and unproductive isolation. The proposed framework is applicable to heterogeneous multi-agent reinforcement learning systems and can potentially be adapted to language-model agents, robotic teams, distributed optimization systems, and other intelligent systems. We formulate the problem mathematically, describe an implementable algorithm, and establish research hypotheses and an experimental protocol for evaluating learning efficiency, collective performance, robustness, diversity, and communication costs. The framework is presented as a research proposal requiring empirical and, where applicable, theoretical validation rather than as an experimentally established superior method.