Mastering Advanced Topics in Reinforcement Learning
A six‑question assessment that pushes the limits of your RL expertise.
Quiz Questions
Answer all questions below and test your knowledge.
- 1
Which formulation represents the policy gradient theorem for a parameterized stochastic policy πθ?
- 2
In TD(0) learning, how is the temporal‑difference error δt computed at time step t?
- 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?
- 4
Which method is specifically introduced to stabilize deep Q‑network training by reducing the moving‑target problem?
- 5
For environments with continuous actions, which algorithm merges deterministic policy gradient with off‑policy data to achieve sample efficiency?
- 6
When using linear function approximation, which condition guarantees convergence of Q‑learning to the optimal value function?
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