Mastering Advanced Topics in Reinforcement Learning

A six‑question assessment that pushes the limits of your RL expertise.

reinforcement learningexplorationactor-criticvalue iterationoptimal controltemporal differencedeep RLreward shapingQ-learningpolicy gradients
Difficulty:HARD

Quiz Details

Questions6
CategoryArtificial Intelligence & Machine Learning
DifficultyHARD
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Quiz Questions

Answer all questions below and test your knowledge.

  1. 1

    Which formulation represents the policy gradient theorem for a parameterized stochastic policy πθ?

    Question 1
  2. 2

    In TD(0) learning, how is the temporal‑difference error δt computed at time step t?

    Question 2
  3. 3

    Which exploration strategy ensures that every state–action pair is visited infinitely often, thereby meeting the sufficient condition for asymptotic optimality in tabular Q‑learning?

    Question 3
  4. 4

    Which method is specifically introduced to stabilize deep Q‑network training by reducing the moving‑target problem?

    Question 4
  5. 5

    For environments with continuous actions, which algorithm merges deterministic policy gradient with off‑policy data to achieve sample efficiency?

    Question 5
  6. 6

    When using linear function approximation, which condition guarantees convergence of Q‑learning to the optimal value function?

    Question 6

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