Consider 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.
Which of these statements is true regarding the rewards obtained in an MDP?
Consider a reinforcement learning agent trying to balance a pole in a continuous environment. The agent receives a reward of +1 for each time step the pole remains balanced and 0 otherwise. Which of the following statements accurately describes the differences between Monte Carlo and Temporal Difference (TD) learning in this scenario?
Monte Carlo methods update the value function based on complete episodes, while TD methods update after each step.
TD methods are guaranteed to converge to the optimal policy, while Monte Carlo methods may not converge.
Monte Carlo methods are less sensitive to the choice of the discount factor compared to TD methods.
TD methods are more effective in environments with high variance and stochasticity compared to Monte Carlo methods.
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 QDB3: 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 QDB3 at a glance |
|---|---|
| Term | May 2024 term |
| Subject | Reinforcement Learning |
| Course code | BSDA5007 |
| Questions | 22 |
| Marks | 50 |
| Duration | 180 min |
| MCQ | 15 |
| MSQ | 2 |
| Numerical | 5 |
| Official paper | IIT M DEGREE AN EXAM QDB3 01 Sep 2024 |
| Negative marking | No negative marking. |
| Updated |
