Question 1
The discount factor used during option execution
Question 2
In DDPG, exploration is typically achieved by:
Randomly sampling from the replay buffer with higher priority
Injecting noise into the critic network parameters
Adding time-correlated noise (e.g., Ornstein-Uhlenbeck) or Gaussian noise to the deterministic policy output
Question 3
Reduces variance without introducing bias
Eliminates all bias in the gradient estimate
Makes the algorithm off-policy
Removes the need for a value function approximator
18 more questions in this paper
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More on the Reinforcement Learning End Term 10 May 2026 Set S2 paper
The IIT Madras BS Reinforcement Learning (Reinforcement Learning) End Term paper sat on 10 May 2026, in the January 2026 term, set S2: 21 questions for 30 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 10 May 2026 Set S2 at a glance |
|---|---|
| Term | January 2026 term |
| Subject | Reinforcement Learning |
| Course code | BSDA5007 |
| Questions | 21 |
| Marks | 30 |
| Duration | 180 min |
| MCQ | 8 |
| MSQ | 7 |
| Numerical | 6 |
| Official paper | Reinforcement Learning 10 May 26 (Session 2) |
| Negative marking | No negative marking. |
| Updated |