Expert-Level Reinforcement Learning Hard Quiz

Explore intricate RL mechanisms through precise queries on advanced algorithms, convergence proofs, and strategic exploration tactics.

#Policy Gradients#Inverse RL#Continuous Control#Eligibility Traces#Proximal Optimization#Deep Q-Networks#Temporal Difference#Actor-Critic

Total Questions

10

Difficulty Level

hard

Progress

0/0

0%

Quiz Questions

Answer all questions below and test your knowledge.

1
In the policy gradient theorem, the gradient of expected return with respect to policy parameters is proportional to:
Question 1
2
What distinguishes off-policy from on-policy evaluation in TD methods?
Question 2
3
In SARSA(λ), eligibility traces update which components?
Question 3
4
The advantage function A^π(s,a) is defined as:
Question 4
5
TRPO optimizes policies by constraining:
Question 5
6
In DQN, experience replay stabilizes training by:
Question 6
7
Soft Actor-Critic maximizes entropy-augmented reward to encourage:
Question 7
8
Hindsight Experience Replay (HER) reframes failed trajectories by:
Question 8
9
The Bellman optimality equation for V*(s) is:
Question 9
10
In continuous action spaces, DDPG uses a deterministic policy μ(s|θ^μ) unlike stochastic policies because:
Question 10