Reinforcement Learning Week 3 NPTEL Assignment Answers 2025

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✅ Subject: Reinforcement Learning
📅 Week: 3
🎯 Session: NPTEL 2025 July-October
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NPTEL Reinforcement Learning Week 3 Assignment Answers 2025

1. The baseline in the REINFORCE update should not depend on which of the following (without voiding any of the steps in the proof of REINFORCE)?

• r_n−1
• r_n
• Action taken(a_n)
• None of the above

Answer : See Answers

2. Which of the following statements is true about the RL problem?

• Our main aim is to maximize the cumulative reward.
• The agent always performs the actions in a deterministic fashion.
• We assume that the agent determines the next state based on the current state and action
• It is impossible to have zero rewards.

Answer :

3. Let us say we are taking actions according to a Gaussian distribution with parameters µ and σ. We update the parameters according to REINFORCE and a_t denote the action taken at step t.

Which of the above updates are correct?

• (i), (iii)
• (i), (iv)
• (ii), (iv)
• (ii), (iii)

Answer :

4.

Answer :

5. Consider the following policy-search algorithm for a multi-armed binary bandit:

where is 1 if a=at and 0 otherwise. Which of the following is true for the above algorithm?

• It is L_R−I algorithm.
• It is L_R−ϵP algorithm.
• It would work well if the best arm had probability of 0.9 of resulting in +1 reward and the next best arm had probability of 0.5 of resulting in +1 reward
• It would work well if the best arm had probability of 0.3 of resulting in +1 reward and the worst arm had probability of 0.25 of resulting in +1 reward

Answer : See Answers

6. Assertion: Contextual bandits can be modeled as a full reinforcement learning problem.
Reason: We can define an MDP with n states where n is the number of bandits. The number of actions from each state corresponds to the arms in each bandit, with every action leading to termination of the episode, and giving a reward according to the corresponding bandit and arm.

• 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 and Reason is false
• Both Assertion and Reason are false

Answer :

7. Let’s assume for some full RL problem we are acting according to a policy π. At some time t, we are in a state s where we took action a₁. After few time steps, at time t’, the same state s was reached where we performed an action a₂(≠a₁). Which of the following statements is true?

• π is definitely a Stationary policy
• π is definitely a Non-Stationary policy
• π can be Stationary or Non-Stationary

Answer :

8. Stochastic gradient ascent/descent update occurs in the right direction at every step

• True
• False

Answer :

9. Which of the following is true for an MDP?

Answer :

10. Remember for discounted returns,

Gt=rt+γrt+1+γ2rt+2+...

Where γ is a discount factor. Which of the following best explains what happens when γ>1, (say γ=5)?

• Nothing, γ>1 is common for many RL problems
• Theoretically nothing can go wrong, but this case does not represent any real world problems
• The agent will learn that delayed rewards will always be beneficial and so will not learn properly.
• None of the above is true.

Answer : See Answers