HiveMind AI: Emergent Intelligence through Multi-Agent Systems using PPO-based CTDE Architecture

Abstract

Multi-agent systems (MAS) represent an important paradigm for developing a model of distributed intelligence through interactions in a complex environment where several agents cooperate to complete a certain mission or accomplish common goals. However, due to some limitations of traditional solutions based on rule-based coordination and independent learning, the development of emergent intelligence is complicated by the lack of scalability, adaptability, and stability. In this paper, the problem of emergent intelligence formation in a multi-agent system was solved by applying the concept of Deep Reinforcement Learning (DRL) to create a fully-fledged HiveMind AI simulation framework. In particular, the authors propose to use Proximal Policy Optimization in conjunction with the CTDE architecture to build a stable multi-agent system. To implement the idea of emerging intelligence, the process of building the corresponding system went through several stages, which included not only designing an environment but also evaluating different approaches in order to choose the most effective and stable solution. Thus, both PPO and Soft Actor Critic algorithms were considered; in particular, the evaluation was performed in the same conditions and using the same criteria. According to the experimental results, the implementation of PPO resulted in reaching 90% of successes compared to about 16% achieved when SAC was used. Therefore, PPO is a better choice when implementing CTDE-based multi-agent system. Moreover, the current implementation includes an interactive module allowing to estimate performance indicators such as reward, collision rate, synchronization level, etc.

Introduction

The text discusses the development of HiveMind AI, a scalable multi-agent system (MAS) framework that uses Deep Reinforcement Learning (DRL) to enable intelligent coordination among multiple agents in dynamic environments. Multi-agent systems are widely used in areas such as traffic optimization, swarm robotics, distributed control systems, and simulations. Traditional MAS approaches rely on rule-based programming or centralized control, which work in simple settings but struggle in complex, changing environments because they lack adaptability and scalability.

To overcome these limitations, the paper proposes the use of Deep Reinforcement Learning, where agents learn through interaction with the environment rather than predefined instructions. However, applying DRL in multi-agent settings introduces challenges such as non-stationarity, unstable learning, inefficient coordination, and convergence issues because agents continuously change their behaviors while learning.

The proposed solution, HiveMind AI, uses the Proximal Policy Optimization (PPO) algorithm with a Centralized Training with Decentralized Execution (CTDE) architecture. During training, a centralized critic has access to global information to stabilize learning and reduce non-stationarity, while during execution, each agent acts independently using only local observations. This approach balances collaboration and autonomy.

The literature review highlights the evolution of multi-agent reinforcement learning (MARL), from rigid rule-based systems to independent reinforcement learning and eventually CTDE-based approaches. PPO is recognized for its stability, while Soft Actor-Critic (SAC) improves exploration but may cause instability. Existing research still lacks unified platforms that integrate simulation, learning, testing, evaluation, and visualization, as well as reliable comparisons between algorithms like PPO and SAC in multi-agent settings.

The research gap analysis identifies unresolved issues such as poor adaptability of traditional systems, weak coordination in independent learning, lack of proper evaluation metrics, and absence of integrated frameworks for MARL experimentation. The project aims to address these gaps by creating a unified and scalable framework.

HiveMind AI’s proposed architecture includes multiple agents operating in a shared environment with shared policy networks. The system uses reward shaping techniques, such as progress rewards and collision penalties, to encourage cooperative behavior. Experiments comparing PPO and SAC showed that PPO achieved better stability and performance, making it the preferred algorithm for the framework.

Conclusion

HiveMind AI is a multi-agent RL framework based on PPO CTDE architecture that aims at facilitating emergent intelligence. The framework has been successful in overcoming common problems such as instability, lack of coordination, and scalability in multi-agent settings. Based on the well-planned implementation and thorough experiments carried out, the framework is highly efficient, showing a high level of success rate of 90% along with consistent behavior in coordination. Through comparative studies, PPO algorithm has proven to perform better than SAC algorithm in this context. Future research could involve scalability in terms of increasing agents\' numbers, introducing communication, and using the framework in practical situations.

References

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Copyright

Copyright © 2026 Mohammed Muneeb, Syed Husamuddin, Mohammed Abdul Samad, Mr. K. Murlidhar. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.