- 具备Python编程经验
- 对深度学习和机器学习概念有基本了解
- 了解强化学习中使用的算法和数学概念
受众
- 数据科学家
- 机器学习从业者
- AI研究人员
强化学习是机器学习中一个强大的分支，智能体通过与环境互动来学习最佳行动。本课程向参与者介绍高级强化学习算法及其使用Google Colab的实现。参与者将使用TensorFlow和OpenAI Gym等流行库，创建能够在动态环境中执行决策任务的智能体。
本次由讲师主导的培训（线上或线下）面向希望深入了解强化学习及其在AI开发中实际应用的高级专业人士，使用Google Colab。
在本培训结束时，参与者将能够：
- 理解强化学习算法的核心概念。
- 使用TensorFlow和OpenAI Gym实现强化学习模型。
- 开发通过试错学习的智能体。
- 使用Q学习和深度Q网络（DQNs）等高级技术优化智能体性能。
- 使用OpenAI Gym在模拟环境中训练智能体。
- 部署强化学习模型以用于实际应用。
课程形式
- 互动讲座与讨论。
- 大量练习与实践。
- 在实时实验室环境中进行动手操作。
课程定制选项
- 如需为本课程定制培训，请联系我们安排。
Reinforcement Learning 简介
- 什么是强化学习？
- 关键概念：智能体、环境、状态、动作和奖励
- 强化学习中的挑战
探索与利用
- 在RL模型中平衡探索与利用
- 探索策略：epsilon-greedy、softmax等
Q学习与深度Q网络（DQNs）
- Q学习简介
- 使用TensorFlow实现DQNs
- 通过经验回放和目标网络优化Q学习
基于策略的方法
- 策略梯度算法
- REINFORCE算法及其实现
- 演员-评论家方法
使用OpenAI Gym
- 在OpenAI Gym中设置环境
- 在动态环境中模拟智能体
- 评估智能体性能
高级Reinforcement Learning技术
- 多智能体强化学习
- 深度确定性策略梯度（DDPG）
- 近端策略优化（PPO）
部署Reinforcement Learning模型
- 强化学习的实际应用
- 将RL模型集成到生产环境中
总结与下一步
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