工作内容
根据实际业务需求开发高效可靠的游戏AI解决方案,探索深度学习/强化学习技术在游戏AI中的应用和实现,包括:
1. 多智能体强化学习:在竞争或合作游戏中训练强化AI模型,设计新的算法以提高多智能体强化学习的训练效率和有效性;
2. 模仿学习:使用人类玩家的对局数据,训练具有不同风格的模仿学习AI模型;
3. GAIL:使用少量人类玩家的对局数据,支持游戏中强化学习AI模型的训练,设计算法以提高AI模型的强度和拟人度。
4. 关注学术界和业界的最新研究成果,并进行高效的复现和应用。
5. 与组织内的其他团队进行沟通交流,探索先进的游戏AI设计方案,以提高玩家的游戏体验。
能力要求
1. 计算机、运筹学、数学或相关领域博士学位。
2. 在相关领域有杰出工作成果(包括但不限于发表的论文,专利、开源项目等),并有游戏AI和强化学习的相关经验。
3. 在以下深度强化学习领域有相关经验:无模型强化学习,模仿学习和逆强化学习,多智能体强化学习,强化学习探索,多任务强化学习,分层强化学习。
4. 至少精通一种编程语言,熟悉TensorFlow,Caffe,PyTorch等深度学习框架。
5. 卓越的分析问题和解决问题能力,对解决挑战性问题充满热情。
6. 良好的抗压能力;对业务意图有很好的理解。
首选资格:
1. 有基于大语言模型的Agents开发经验。
2. 在顶级AI会议(NeurIPS,IJCAI,AAAI,ICML,ICLR等)上发表过研究成果。
3. 热衷于游戏行业,有游戏AI制作经验。
4. 出色的沟通和协作技巧。
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