Intense Reinforcement Learning Pro Quiz

Test advanced RL proficiency through complex scenarios and theoretical depth in this demanding 7-question assessment.

#reinforcement learning#deep RL#Q-learning#policy gradients#model-free RL#off-policy algorithms#actor-critic methods

Total Questions

7

Difficulty Level

hard

Progress

0/0

0%

Quiz Questions

Answer all questions below and test your knowledge.

1
In the Bellman optimality equation for the action-value function Q*, what does it represent?
Question 1
2
What key difference distinguishes SARSA from Q-learning updates?
Question 2
3
The policy gradient theorem states that the gradient of expected return J(θ) is proportional to what?
Question 3
4
In off-policy learning with importance sampling, why is the ratio π(a|s)/μ(a|s) used?
Question 4
5
What innovation in Double DQN addresses the overestimation bias in standard DQN?
Question 5
6
PPO improves upon TRPO by approximating the constrained optimization with what?
Question 6
7
In multi-agent reinforcement learning, what does a Nash equilibrium represent?
Question 7