Quiz jàng buy dooleel

Sooy def quiz bi, tontu yi ak leeral yi dañuy feeñ.

1. Benn robotu depo dafay jéema jaar ci ale bimu xamul ngir xam ndax dafay wàññi diiru joxe gi. Ban wàllu jënd ak jaay mooy lii?
   • Exploration: testing an action whose value is uncertain
   • Exploitation: choosing the currently highest-valued action
   • Reward shaping: changing the objective after acting
   • Imitation: copying a labeled action
2. Neexal yi dañuy ñëw ci 2, 0 ak 5 ci benn episode bu am ñatti jéego. Ak facteur de réduction 0.9, lan mooy retour ci jéego bu njëkk bi?
   • 7
   • 6.05
   • 6.5
   • 4.05
3. Designer bi dafay yokk gamma ci mébet buy wàññi ndàmpaay. Luy soppiku?
   • The agent stops exploring
   • The reward function becomes a probability distribution
   • Future rewards count more relative to immediate rewards
   • Past rewards are counted again
4. Politik du am mbégte ci jëf yu bari yu toppalante. Ban laaj ci wàllu leble mooy jog?
   • Which input column has the longest name?
   • Whether every action must receive the same reward
   • Whether the final reward erases all prior observations
   • Which earlier actions contributed to the eventual outcome
5. Agent jeu bi dafay am poñ su amee bonus bu amul benn loraange, ba noppi bàyyi xel ci misioŋ bi ndax bonus bi yomb naa baamtu. Ban njuumte la lii di wane?
   • Reward hacking: optimizing a proxy reward in a way that misses the intended goal
   • Catastrophic forgetting
   • Exploration collapse caused by gamma
   • A transition model error
6. A robot policy performs well in simulation. Why does it still need evaluation for its intended real deployment?
   • Simulation success proves every real-world action is safe
   • The simulator may rely on assumptions that fail outside it
   • A simulation prevents all reward loopholes
   • Real observations always match simulated ones
7. A delivery task has delayed payoffs: a longer route now may prevent a costly delay later. Why does a one-step reward view miss the trade-off?
   • It assumes every action is random
   • It cannot represent observations
   • It ignores consequences that arrive in later states
   • It forces rewards to be positive
8. A team changes the reward from “deliver safely” to “deliver quickly” and sees faster but riskier behavior. What should it conclude?
   • A faster policy proves the original safety objective was met
   • The discount factor alone guarantees safe behavior
   • Human preferences are unnecessary once training converges
   • Reward design affects behavior, so safety must be represented and evaluated explicitly