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Refer to figure below to answer this question.  If the actual market price were fixed at $3 per unit,

Q3: (3 points) Answer True or False Only: To identify SVM margin, we only need support vectors.

Q1: (3 points) Answer True or False only: In RL, the agent learns from labeled data, which we call trajectories, provided during training.

Q7: (8 points)After taking the action suggested in the previous question, suppose the discount factor is γ = 0.9, the state transfers from s₂ to s₄ after taking action aₜ, and the reward r is 0.6. Please update the Q-table and write down the updated Q-table. Note: Only one value in the table needs updating, and you might need the Bellman Equation:

Q5: (6 points)Give a scenario that hard margin SVM doesn’t work well, and we need soft margin SVMs.

Q3: (3 points) Answer True or False only: In Boosting, the performance of the final model heavily depends on the performance of the individual weak learners.

Q6: (5 points)Why do we need to use a kernel function in SVM? What does a kernel function try to measure?

Q1: (4 points)What is the two main objective of a Support Vector Machine?

Q4: (3 points)List the names of at least two types of SVM kernels.

Q4: (3 points) Answer True or False only: In DQN, the Q value function is instantiated by a neural network.