Consider the following assertion reason pair:
Assertion: Reinforcement learning is a type of unsupervised learning algorithm as both don’t have correct labels.
Reason: In unsupervised learning, a reward like quantity is not maximized.
Assertion and Reason are both true and Reason is a correct explanation of Assertion.
Assertion and Reason are both true and Reason is not a correct explanation of Assertion.
Assertion is true but Reason is false.
Assertion is false but Reason is true.
Consider a reinforcement learning agent navigating a grid world environment. The agent receives rewards of +1 for reaching the goal state and 0 otherwise. Which of the following statements accurately describes the differences between Monte Carlo and TD learning in this scenario?
Monte Carlo updates are unbiased estimators of the true value function, while TD updates may introduce bias.
TD updates are guaranteed to converge to the optimal value function, while Monte Carlo updates may not converge.
Monte Carlo updates require less memory and computational resources compared to TD updates.
TD updates are more robust to noise and stochasticity in the environment compared to Monte Carlo updates.
None of these
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The IIT Madras BS Reinforcement Learning (Reinforcement Learning) End Term paper sat on 1 Sept 2024, in the May 2024 term, set QDB1: 22 questions for 50 marks in 180 minutes. The first 3 questions are below. Sign in with Google — it is free — to see the whole paper with its answers and explanations, in learning mode or as a timed mock test.
| Feature | Reinforcement Learning End Term 1 Sept 2024 Set QDB1 at a glance |
|---|---|
| Term | May 2024 term |
| Subject | Reinforcement Learning |
| Course code | BSDA5007 |
| Questions | 22 |
| Marks | 50 |
| Duration | 180 min |
| MCQ | 14 |
| MSQ | 3 |
| Numerical | 5 |
| Official paper | IIT M DEGREE AN EXAM QDB3 01 Sep 2024 |
| Negative marking | No negative marking. |
| Updated |
