用区块链实现众包深度强化学习服务，降低使用门槛。
Blockchain-based Crowdsourced Deep Reinforcement Learning as a Service
- 通过区块链众包模式整合人力与算力资源训练DRL模型
- 支持预训练模型共享，提升新任务训练效率
- 基于联盟链+智能合约保障流程透明可信
深度强化学习（DRL）虽能解决复杂问题，但因需专业技能、高算力及模型设计能力，难以普及。为此，本文提出一种基于区块链的众包DRL即服务（DRLaaS）框架，支持两类任务：DRL训练与模型共享。用户可借助众包工作者的专业知识和计算资源完成训练；也可获取他人分享的预训练模型，通过知识迁移加速新模型开发。该框架基于联盟链构建，利用智能合约管理任务分配与模型调度，并通过IPFS存储模型数据以确保不可篡改。在多个DRL应用上验证了其有效性。
原文摘要 · Abstract (English)
Deep Reinforcement Learning (DRL) has emerged as a powerful paradigm for solving complex problems. However, its full potential remains inaccessible to a broader audience due to its complexity, which requires expertise in training and designing DRL solutions, high computational capabilities, and sometimes access to pre-trained models. This necessitates the need for hassle-free services that increase the availability of DRL solutions to a variety of users. To enhance the accessibility to DRL services, this paper proposes a novel blockchain-based crowdsourced DRL as a Service (DRLaaS) framework. The framework provides DRL-related services to users, covering two types of tasks: DRL training and model sharing. Through crowdsourcing, users could benefit from the expertise and computational capabilities of workers to train DRL solutions. Model sharing could help users gain access to pre-trained models, shared by workers in return for incentives, which can help train new DRL solutions using methods in knowledge transfer. The DRLaaS framework is built on top of a Consortium Blockchain to enable traceable and autonomous execution. Smart Contracts are designed to manage worker and model allocation, which are stored using the InterPlanetary File System (IPFS) to ensure tamper-proof data distribution. The framework is tested on several DRL applications, proving its efficacy.
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