International Research Journal of Engineering and Technology (IRJET) Volume: 12 Issue: 05 | May 2025 www.irjet.net
e-ISSN: 2395-0056 pISSN: 2395-0072
AI-Driven Dynamic Self-Optimization of TRON Smart Contracts Using Reinforcement Learning Sahil Patil1, Rahul Pal2 IDOL, Mumbai University, Mumbai, India 1Masters in Computer Application, Mumbai University, Maharashtra, India
2Masters in Computer Application, Mumbai University, Maharashtra, India
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Abstract— Blockchain-based smart contracts have
revolutionized the landscape of decentralized applications (DApps) by enabling autonomous, tamper-proof, and selfexecuting transactions without the need for intermediaries. Despite their transformative potential, traditional smart contracts suffer from inherent limitations such as static computational logic, lack of adaptability, and inefficiencies caused by fluctuating gas fees and network congestion. These constraints hinder their scalability and costeffectiveness, particularly in real-time, high-frequency transaction environments.To overcome these challenges, this research proposes an AI-driven dynamic self-optimizing smart contract framework that leverages Reinforcement Learning (RL) and real-time blockchain analytics. The core innovation lies in the contract’s ability to autonomously learn from historical transaction patterns, monitor live network states, and dynamically reconfigure its execution strategies. This includes adjusting gas fees, reordering execution priorities, and selectively modifying contract logic based on changing blockchain conditions, without compromising immutability or security.We implement the proposed system on the TRON blockchain’s Shasta Testnet, chosen for its high throughput and developer-friendly environment. The AI model is trained using Q-learning and integrates with the smart contract via a modular API layer, allowing seamless decision-making in a decentralized setup. Our experimental evaluation highlights significant improvements: a 20–30% reduction in average gas fees, a 26% enhancement in execution speed, and a 66% reduction in failed or reverted transactions under variable load conditions.The results validate the feasibility and benefits of embedding AI directly into blockchain infrastructures. By bridging the domains of artificial intelligence and decentralized computing, this research introduces a scalable, self-adaptive, and cost-efficient paradigm for the next generation of DApps—paving the way for intelligent, autonomous contract systems that respond in real time to dynamic environments. Keywords—Smart Contracts, Blockchain, AI, Reinforcement Learning, Gas Fees Optimization, TRON Blockchain, Dynamic Execution
© 2025, IRJET
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Impact Factor value: 8.315
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Blockchain technology has revolutionized the digital geography by furnishing a decentralized, inflexible, and transparent terrain for executing deals and agreements. At the core of this metamorphosis lies the conception of smart contracts — tone- executing programs that automate contractual agreements without the need for centralized interposers. These smart contracts are decreasingly being espoused across different diligence, including finance, force chain, healthcare, and decentralized operations( dApps), for their capability to streamline processes, reduce mortal error, and apply unsure prosecution. Despite their eventuality, traditional smart contracts parade a critical limitation static prosecution sense. Once stationed on the blockchain, a smart contract's geste remains fixed, rendering it unfit to acclimatize to the dynamic nature of blockchain surroundings. This severity becomes a significant tailback in real- world scripts, where conditions similar as network traffic, gas figure volatility, and shifting computational demands can drastically affect the performance, cost, and trustability of smart contract prosecution. Smart contracts operating under high network business or unforeseen changes in resource demand frequently face prosecution detainments, increased sale costs, or indeed failures due to out- of- gas crimes. also, being smart contracts warrant the capability to learn from former relations or optimize themselves in response to changing external conditions. These challenges punctuate the critical need for intelligent, adaptive smart contract mechanisms that can respond to the complications of real- world blockchain ecosystems. To address these limitations, this exploration introduces an AI- driven dynamic tone- optimizing smart contract frame, which leverages Machine literacy( ML) and underpinning literacy( RL) ways to enable smart contracts to evolve over time. The proposed model continuously monitors current blockchain conditions and intelligently adjusts parameters similar as gas freights, prosecution strategies, and contract sense to enhance performance and cost- effectiveness. Specifically, our frame is enforced on the TRON blockchain( Shasta Testnet) — named for its high outturn, low quiescence, and low- cost sale capabilities making it an ideal platform for real- time trial. The intelligent contract ISO 9001:2008 Certified Journal
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