AlphaGo ak AlphaZero Quiz

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

1. Lu tax ñu jàpp ni jeu Go lu jafe la ci ordinatër yi?
   • Amna ay posisioŋ yu bari yuñ mëna nekk ci wàllu astronomie
   • Amna yenn jéego yu mëna am
   • Ci këyit kese lañu koy jouer
   • Amul ay sàrt
2. Kan moo AlphaGo daanele 4-1 ci 2016?
   • Gary Kasparov
   • Lee Sedol
   • Magnus Karlsen
   • amateur bu bari
3. Which three games did AlphaZero’s paper evaluate?
   • Go, chess, and shogi
   • Poker, bridge, and backgammon
   • Atari, StarCraft, and Dota
   • Only Go and chess
4. Ban xeetu seet la AlphaZero di boole ak reso neuronal bi?
   • Seetug binaire ci téere telefon
   • Regresioŋ ligneer
   • Bubble tànnal
   • Seetug garabu Monte Carlo
5. Lan mooy ñaari mbir yi reso neuronal bu AlphaZero di wax ni ñooy jiite seetlu bi?
   • Politik (jëf yu baax) ak valeur (kuy jël ndam li)
   • Klimaa bi ak waxtu wi
   • Turu noon bi ak at yi mu am
   • Kulòor yi ak son yi
6. What did MuZero add to the AlphaZero-style planning approach?
   • A requirement for human game records
   • A learned model of reward and environment dynamics used for planning without being given the rules
   • A replacement for all tree search
   • A fixed database of every possible board position
7. What information did AlphaZero need in its board-game experiments besides self-play?
   • A database of professional games
   • A complete endgame table for every position
   • The rules of the games
   • Human rankings for every possible move
8. How did the original AlphaGo system combine supervised and reinforcement learning?
   • It trained a policy from expert games and then improved through self-play
   • It used only random moves and never trained on human games
   • It learned the game rules from images without playing
   • It relied only on a fixed database of winning games