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强化学习 - ★★形考作业2(总成绩占比10%)

课程名称:强化学习 发布教师:卢艳君 作业来源:第五单元 强化学习的实验工具和环境 作业满分:100.0分 发布时间:2025-07-06 作业要求:根据第四~五单元学习内容完成本次形考作业,本次作业题型包括单选题、多选题和简答题。通过本次作业的练习,大家应该掌握策略下状态-动作价值函数的含义和计算方法、Sarsa的原理和方法、Q-learning的原理和方法、DQN的原理和方法、了解强化学习实验环境Gym等。

单选题

1. 下列关于状态价值V(s)的表达式正确的是( ) 注:下列选项中 表示策略, 表示折扣因⼦, 表示t时刻奖励, 表示t时刻状态, 表示t时刻动作。 (分值:5.0分)

A.

B.

C.

D.

2. 下列关于策略下状态价值 的表达式正确的是( ) 注:下列选项中 表示策略, 表示折扣因⼦, R t 表示t时刻奖励, S t 表示t时刻状态, A t 表示t时刻动作。 (分值:5.0分)

A.

B.

C.

D.

3. 在三连棋游戏中,如果玩家的第⼀步棋选择⾛中间,那么下列分析错误的是( ) (分值:5.0分)

A. 电脑下⼀步选择⻆或边的概率各为1/2。

B. 若电脑选择边,则⼈类玩家有必胜策略。

C. 若电脑选择⻆,则⼈类玩家的最佳动作是选择边,有的概率可以获胜。

D. 可以获得的期望奖励为1*1/2+1*(1/2)*(2/3)=5/6。

4. Q表格中Q 函数的意义是() (分值:5.0分)

A. 在强化学习里面,我们可以每走一步更新一次Q 表格,用下一个状态的Q 值来更新当前状态的Q 值。

B. Q 表格,一般它的行数是所有动作的数量。

C. 最开始的时候,Q 表格会全部初始化任意值。

5. Q 表格的概念中,() (分值:5.0分)

A. 表格中每一个坐标点对应某时刻智能体的状态

B. 以每执行若干步,就对Q 表格进行更新

C. 与时间差分法毫无关系

D. 其表示形式是表格,其中表格的横轴为动作(智能体的动作),纵轴为环境的状态。

6. 蒙特卡洛方法() (分值:5.0分)

A. 蒙特卡洛方法需要马尔可夫决策过程的状态转移函数和奖励函数。

B. 蒙特卡洛方法需要更新多条轨迹的状态。

C. 适用于环境未知的情况。

D. 蒙特卡洛方法与动态规划是一个概念。

7. Q-learning算法中,Q函数是 (分值:5.0分)

A. 状态-动作值函数

B. 状态函数

C. 估值函数

D. 奖励函数

8. Bellman方程在计算Q(s, a)时,折扣因子γ的作用是什么? (分值:5.0分)

A. 它决定了未来奖励的重要性

B. 它用来衡量当前奖励的重要性

C. 它是奖励的直接来源

D. 它只影响状态的转换

9. 动态规划的核心思想是什么? (分值:5.0分)

A. 通过分治将问题拆分为更小的子问题

B. 通过试探和回溯来探索所有可能的解决方案

C. 将问题拆解为子问题并通过存储子问题的解来避免重复计算

D. 通过贪心选择每次局部最优解来得到全局最优解

10. 在蒙特卡洛方法中,估计一个问题的期望值通常依赖于: (分值:5.0分)

A. 通过穷举所有可能的情况来计算期望

B. 随机生成多个样本,并计算它们的平均值

C. 通过公式直接计算期望

D. 使用动态规划来避免重复计算

11. 下列哪个是Q-learning算法的特点? (分值:5.0分)

A. 需要知道环境的转移概率和奖励函数

B. 依赖于策略的梯度方法

C. 是一种无模型的强化学习方法

D. 只能处理离散动作空间

12. Sarsa和Q-learning的主要区别是: (分值:5.0分)

A. Sarsa是基于策略的,而Q-learning是基于值的

B. Q-learning是基于策略的,而Sarsa是基于值的

C. Sarsa更新时采用当前策略的动作,而Q-learning采用最优动作

D. Sarsa通过回放来更新Q值,而Q-learning不使用回放

多选题

1. 策略迭代法的核⼼步骤包括哪两个部分( ) (分值:5.0分)

A. 策略评估

B. 状态价值计算

C. 策略提升

D. 使⽤贪心策略

2. 对于Gym实验环境库,使用()指令可以加载环境,使用()可以初始化环境,使用()可以执行一步环境。 (分值:5.0分)

A. env.close()

B. env=gym.make(环境名)

C. env.reset()

D. env.step(动作)

E. env.render()

3. 在强化学习中,以下哪些因素会影响状态-动作价值函数Q(s, a)的计算? (分值:5.0分)

A. 当前状态s和动作a

B. 状态转移概率

C. 即时奖励R(s, a)

D. 折扣因子γ

E. 未来状态s'及其对应的动作a'的价值

4. 下列哪些是常见的用于强化学习实验的环境和框架? (分值:5.0分)

A. OpenAI Gym

B. TensorFlow

C. PyTorch

D. Scikit-learn

E. Unity ML-Agents

F. Pandas

G. Keras

简答题/计算题

1. 简述OpenAI Gym的作用及如何使用它来创建实验环境。 (分值:10.0分)

2. 什么是深度Q网络(DQN),它是如何解决Q-learning中的问题的? (分值:10.0分)

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渝粤题库 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+Exx1nkkMJTWO2GoXIgI8Uha9FQuoRTNLq00fBSxIyz4U4zQsh6VKvExBexhXiPc6dbLyVVUfd14347RBDJYRGwnDxE0jQd32J41APssb3BGHn8+n1CeT6mRASElLfdpZcS8gisfu/pQpb23DJp1hcErl7MnFcfIFwmJL3ioTh4kUGkDmHoqQYXaFx+qVGmkUVjOFyXk7+fBhCfnQdSKbvf0qfSjqypUPUhYTh4kWRQ3MCSKTsPUQToZg+yzvRUINixISdQWwfZ9iLhoVliIp6UcdEuCraDPwWM2ye4uSFZ/RKBSFpuHQgFJWjJuNrq9qzXx82BBUxlxEYa4de9sz7LaNikMStJ3zJaouBs6bA5t4ZXIivXbOEG4NjG/YgfexqLHBuLVNERozwFcxXsWe5cM6tJYb07lhtLpEbl4Cox9fxyGAmAo7thNfC0XCQeT1cC2hrqaCsWO6QSG0E6Yi4mioaMsuzbPofgV+A5HwFI4dMdv8PS12QvsMHf0Tk15gyedfwkJ9cgA8oNiwdBfW5cYaLn4Crga/wRY+O0KVfpa6hifLwp0iqq7bUO2PCj9OgE7gXfyUr8sphPjhX/oBfuiu8ZvSArrxzz01OCWKu/o30L7qjs64yWOoa0C+PwNOk+uqCBXXb0fhxgTYCt55HMk9C3PnpuLHDGwe1J8DTWRN5orWSdW1pSIyKQAT7DsL/DMDmJVMwfPxi/H6XA5j1h4tdjRyzyB2YPycAnK4OaC8xX8bStEZvVze4udW3DYCDsQY0dWvO2SlAQSpik9uhn4NpI4VVQVo7YeKYtog9cwDbTrzGlCEd5Yi08iWy+8/Dd54PQpPeZvd/7x2ObObKd4MxnCcOg0XsWRzYFozUKYPQ+d0HYd8hiutzI985X4HclGyomJmJIh2kOB0J+droUG8IVvhOc3/M36mEef5z4SzzglZxFHPVmlgM8F4Bt1qJih8DBPzcNKSrmOATHVWhu4RXCYXQ7WDKpI3UAzVS131XYSdrGvyX9IIhChB39H9v1q8pjgrafv87js+wp36SEHFKhHerudjuEguvNekYdcAbUzuJDVtlSjh+D41poCdXgVGvUeiXugmuawGvs7/ATRhJ5kfAf8B0nBwtR6pFWSL+PpOPz0d1h2FTdVTCt1UkpoCr1x4djaW9f06YpOqDE+3m1lhTS5tYuNwnv0zsXbHUoUOrsbBAfMxS14VRjUTZul7dLDt8cF8lUS9eFzYdjfHBrb1+Q+P0uYn+lx/h++0jkKhlR3tmWaM0inzmXwYpROK9FGh1t4OxchESb95CbH5jBgNq0DExgWl7W4l8IrHh8rXdiktzTHF73SwsfDoQew98Dweh58XPpZ5bagN5ceKcNNOic/hh2AFY7PgDCx21QV6dwszhh2HptxfLezb5wO8dIuyQXiFbvQ1MFU6VECNb5/9voHH63PzfkzUjQQbCvddi54XHeKlhg+69+8GxKBS7wtLRyuJzDJntheUubZhrZaEEicd+xKIb7TFrrA0Krh7GSbXp2L20Fwz+3VrZzD9Ms+FqRgLhcp9c5DNZzSx1LWijGAWiY6U3CZvywUPBi6d4FJ0LVYuucOxs8gEPX5r5WGg2XM0008xHR5MEappppplmGkOz4WqmmWY+OpoNVzPNNPORAfwflPZNg3To9xAAAAAASUVORK5CYII=" alt="文档图片" style="max-width:100%;height:auto;">
文档图片
国家开放大学 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" alt="文档图片" style="max-width:100%;height:auto;">
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广东开放大学 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