CS7643 last quiz questions and answers
2026\2027 A+ Grade
Reinforcement learning
- correct answer Sequential decision making in an environment with evaluative feedback
Environment: may be unknown, non-linear, stochastic and complex
Agent: learns a policy to map states of the environments to actions
- seeks to maximize long-term reward
RL: Evaluative Feedback
- correct answer - Pick an action, receive a reward
- No supervision for what the correct action is or would have been (unlike supervised learning)
RL: Sequential Decisions
- correct answer - Plan and execution actions over a sequence of states
- Reward may be delayed, requiring optimization of future rewards (long-term planning)
Signature Challenges in RL
- correct answer Evaluative Feedback: Need trial and error to find the right action
Delayed Feedback: Actions may not lead to immediate reward
Non-stationarity: Data distribution of visited states changes when the policy changes
Fleeting Nature: of online data (may only see data once)
MDP
- correct answer Framework underlying RL
, S: Set of states
A: Set of actions
R: Distribution of Rewards
T: Transition probabiliity
y: Discount property
Markov Property: Current state completely characterizes state of the environment
RL: Equations relating optimal quantities
- correct answer 1. V*(S) = max_a(Q*(s, a)
2. PI*(s) = argmax_a(Q*(s, a)
V*(S)
- correct answer max_a (sum_(s') { p(s'|s, a) [r(s, a) + yV*(s')] } )
Q*(s,a)
- correct answer sum_(s') { p(s'|s, a) [r(s, a) + y*max_(a'){Q*(s', a') ] }
Value Iteration
- correct answer v_(i+1) = max_a (sum_(s') { p(s'|s, a) [r(s, a) + yV_(i)(s')] } )
- repeat until convergence
- Time complexity per iteration O(|S^2| |A|)
Policy Iteration
- correct answer Policy Evaluation: Compute V(pi)
Policy Refinement: Greedily change action as per V(Pi) at next states
Why do Policy Iteration: PI_i often converges to PI* sooner than V_PI to V_PI*
- thus requires few iterations
2026\2027 A+ Grade
Reinforcement learning
- correct answer Sequential decision making in an environment with evaluative feedback
Environment: may be unknown, non-linear, stochastic and complex
Agent: learns a policy to map states of the environments to actions
- seeks to maximize long-term reward
RL: Evaluative Feedback
- correct answer - Pick an action, receive a reward
- No supervision for what the correct action is or would have been (unlike supervised learning)
RL: Sequential Decisions
- correct answer - Plan and execution actions over a sequence of states
- Reward may be delayed, requiring optimization of future rewards (long-term planning)
Signature Challenges in RL
- correct answer Evaluative Feedback: Need trial and error to find the right action
Delayed Feedback: Actions may not lead to immediate reward
Non-stationarity: Data distribution of visited states changes when the policy changes
Fleeting Nature: of online data (may only see data once)
MDP
- correct answer Framework underlying RL
, S: Set of states
A: Set of actions
R: Distribution of Rewards
T: Transition probabiliity
y: Discount property
Markov Property: Current state completely characterizes state of the environment
RL: Equations relating optimal quantities
- correct answer 1. V*(S) = max_a(Q*(s, a)
2. PI*(s) = argmax_a(Q*(s, a)
V*(S)
- correct answer max_a (sum_(s') { p(s'|s, a) [r(s, a) + yV*(s')] } )
Q*(s,a)
- correct answer sum_(s') { p(s'|s, a) [r(s, a) + y*max_(a'){Q*(s', a') ] }
Value Iteration
- correct answer v_(i+1) = max_a (sum_(s') { p(s'|s, a) [r(s, a) + yV_(i)(s')] } )
- repeat until convergence
- Time complexity per iteration O(|S^2| |A|)
Policy Iteration
- correct answer Policy Evaluation: Compute V(pi)
Policy Refinement: Greedily change action as per V(Pi) at next states
Why do Policy Iteration: PI_i often converges to PI* sooner than V_PI to V_PI*
- thus requires few iterations
