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CS 7643 Quiz 5|New 49 Questions and Answers|2025 Update|100% Correct-GT 5|New 49 Questions and Answers|2025 Update|100% Correct-GT

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CS 7643 Quiz 5|New 49 Questions and Answers|2025 Update|100% Correct-GT 5|New 49 Questions and Answers|2025 Update|100% Correct-GT

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  • March 22, 2025
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  • CS 7643
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CS 7643 Quiz 5|New 49 Questions and Answers|2025
Update|100% Correct-GT 5|New 49 Questions and
Answers|2025 Update|100% Correct-GT




Reinforcement learning - 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

Experience Replay - ANSWER-- store (s, a, s', r) pairs and
continually update episodes (older samples discarded)

- Train Q-Network on random mini batches of transitions from the
replay memory instead of consecutive examples

- larger the buffer, lower the correlation

Value Based RL Methods Learn, Model Based Methods Learn -
ANSWER-- Q-Functions
- Transition and reward function

, RL: Evaluative Feedback - 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 - 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 - 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 - 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 - ANSWER-1. V*(S) =
max_a(Q*(s, a)
2. PI*(s) = argmax_a(Q*(s, a)

V*(S) - ANSWER-max_a (sum_(s') { p(s'|s, a) [r(s, a) + yV*(s')] } )

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