Adaptive reinforcement learning agents coordinated through blockchain smart contracts for dynamic governance in decentralized autonomous multi-agent ecosystems

The rise of decentralized autonomous systems has accelerated the development of multi-agent ecosystems where independent entities interact, cooperate, and compete to achieve collective goals. Traditional governance models for such ecosystems often struggle with scalability, trust, and adaptability, particularly as agent behaviors evolve dynamically in complex environments. To address these challenges, this study proposes a governance framework that integrates adaptive reinforcement learning (RL) agents coordinated through blockchain-enabled smart contracts. In this approach, reinforcement learning agents continuously adapt policies in response to changing environmental conditions and evolving system objectives. Their adaptive decision-making is augmented by blockchain smart contracts, which provide a tamper-resistant, transparent, and decentralized coordination layer. Smart contracts encode governance rules, enforce accountability, and ensure that cooperative behaviors among agents are aligned with agreed-upon protocols. This integration prevents unilateral manipulation, supports dynamic consensus, and fosters equitable participation across diverse agents. The framework is designed to function across decentralized infrastructures where centralized oversight is infeasible. By embedding governance into programmable contracts, system operations become both autonomous and verifiable, enabling trust in high-stakes contexts such as decentralized finance, energy trading, and smart city management. Simulations demonstrate that combining adaptive RL with blockchain governance enhances stability, resilience, and efficiency under conditions of uncertainty, while reducing risks of collusion or free-riding. This paradigm illustrates a pathway toward self-governing decentralized ecosystems, where intelligent agents not only optimize their actions but also collectively enforce fair, scalable, and transparent governance. It advances both the technical foundations of multi-agent reinforcement learning and the institutional frameworks of decentralized autonomy.