目的 针对开放式环境下个体自主决策易导致群体组织松散且无规划而造成的成本过高、任务超期等问题，研究基于强化学习的开放式众包任务优化。方法 首先，通过效用理论对个体选择是否参与任务及贡献模块的自组织决策行为进行分析与建模;然后，根据马尔科夫决策过程构建众包任务中的状态和动作，以缩短任务耗时与减少任务成本为优化目标设置奖励;最后，基于DDPG强化学习算法构建策略和价值网络对奖金、能力指数要求进行动态设置。结果 仿真实验结果显示，该方法能有效缩短任务耗时，减少成本支出，为开放式众包任务优化提供了一种有效方案。结论 使用深度强化学习方法，对众包中的奖金、能力指数要求进行动态智能调控，实现了任务耗时的缩短和任务成本的降低，为未来在复杂开放式环境中的众包决策提供了借鉴。

Aiming at the problems of loose group organization, high cost and overdue tasks caused by unplanned individual decision-making in open environments, the work aims to study the open crowdsourcing task optimization based on reinforcement learning. Firstly, the self-organizing decision-making behavior of individual selection whether to participate in the task and contribution module was analyzed and modeled by the utility theory. Then, according to the Markov decision process, the state and action in the crowdsourcing task were constructed, and the reward was set to shorten the task time and reduce the task cost. Finally, based on the DDPG reinforcement learning algorithm, the strategy and value network were constructed to dynamically set the bonus and ability index requirements. The simulation results showed that this method could effectively shorten the task time and reduce the cost, which provided an effective solution for open crowdsourcing task optimization. Using the deep reinforcement learning method to dynamically and intelligently regulate the bonus and capability index requirements in crowdsourcing realizes the shortening of task time and the reduction of task cost, and provides a reference for crowdsourcing decision-making in complex open environments in the future.