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Open nowPosted 43 days ago

持续强化学习科研实习生

sii-czxy/campus170 open roles

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Share of postings closed within
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  2. 4.5%3 days
  3. 12.3%7 days
  4. 19.3%14 days
  5. 35.6%30 days
  6. 58.1%60 days
This job: posted 43 days ago

The posting

1. 调研并整理持续强化学习（Continual Reinforcement Learning）、预测知识学习、智能体状态表征、时序抽象和世界模型等方向的前沿论文与开源工作，分析不同方法的核心思想、适用场景和技术联系。 2. 复现代表性持续强化学习算法和智能体学习方法，搭建实验环境与基准任务，完成算法实现、实验配置、性能测试和结果记录。 3. 参与交互式智能体算法研发，围绕预测知识学习、状态表示更新、技能发现（Option Learning）以及模型规划等模块开展研究与实现。 4. 设计并开展算法对比实验和消融实验，分析智能体在预测能力、交互效率、知识迁移、长期适应和持续学习能力等方面的表现，定位算法瓶颈并提出优化方向。 5. 探索基于时序抽象和预测模型的智能体自主学习机制，研究智能体如何通过持续交互积累可验证知识，并优化自身表示与决策策略。 6. 参加课题组讨论并进行阶段性研究汇报，根据项目进展参与算法改进、实验分析和科研成果整理。

1. 招募对象：本科生、硕士研究生。 2. 计算机、人工智能、数学、统计学、自动化等相关专业背景优先。 3. 具备强化学习基础知识，理解以下概念者优先：马尔可夫决策过程（MDP）；价值函数与策略优化；时序差分学习（TD Learning）；模型强化学习（Model-based RL）。 4. 对持续强化学习、智能体自主学习和长期适应问题具有研究兴趣，关注以下方向：在线学习与持续交互；预测知识表示；状态表征学习；时序抽象与技能发现；世界模型与智能体规划。 5. 具备 Python 编程基础，能够使用 AI 编程工具辅助快速实现研究想法、复现算法和搭建实验，并能够理解代码逻辑、调试问题和分析实验结果。 6. 能够阅读英文科研论文和技术资料，具备基础实验设计、算法复现和结果分析能力。 7. 有以下经历者优先：强化学习、深度学习或智能体相关项目经验；世界模型、Agent、机器人学习相关研究经历；算法竞赛、开源项目或论文复现经历。 8. 本科生要求：对强化学习和智能体研究具有兴趣，并具备一定基础；可从文献阅读、算法复现和实验分析等环节逐步参与研究。 9. 硕士研究生要求：具备较好的强化学习理论基础；能够围绕具体研究问题参与方法设计、算法实现和系统验证。 10. 具备较强的主动学习能力和科研探索意识，能够持续提出问题、验证假设并推进研究。 11. 实习安排：线上线下结合；周期3–6个月；每周投入20–30小时。

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