- 熟练掌握Python
- 了解大学微积分和线性代数
- 基本了解概率和统计
- 用Python和Numpy创建机器学习模型的经验
深度强化学习是指“人工智能体”通过反复试验和奖惩来学习的能力。人工智能体旨在模仿人类直接从原始输入（如视觉）获取和构建知识的能力。为了实现强化学习，深度学习和神经网络会被用到。强化学习与机器学习不同，不依赖于有监督和无监督的学习方法。
在这一由讲师引导的现场培训中，学员将在逐步创建深度学习智能体的过程中学习深度强化学习的基础知识。
在本次培训结束后，学员将能够：
- 理解深度强化学习的基本概念，及其与机器学习的区别
- 运用先进的强化学习算法来解决实际问题
- 构建深度学习智能体
受众
- 开发人员
- 数据科学家
课程形式
- 部分讲座、部分讨论、练习和大量实操
介绍
强化学习基础
强化学习基本技术
BURLAP简介
值迭代和策略迭代的收敛
奖赏塑形（Reward Shaping）
探索（Exploration）
泛化（Generalization）
部分可观察的马尔可夫决策过程（POMDP）
选择（Options）
Logistics
TD Lambda
策略梯度（Policy Gradient）
深度Q学习
博弈论（Game Theory）专题
总结和结论
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