Guide-yakabatana mibvunzo · Pakati Nhanho

Monte Carlo Tree Search Quiz

Inotarisa MCTS iteration nhanho, kuongorora kweUCT, kuburitsa simulation, midzi-yekufamba sarudzo uye kuti AlphaGo yakasanganisa sei kutsvaga nemanetiweki.

Nzira dzekutungamira dzinoenderanaMonte Carlo Tree Search
Mubvunzo 1 ye 8

Ndeapi matanho mana makuru eMonte Carlo Tree Search iteration?

Tarisa mubvunzo wese8 mibvunzo

Mhinduro uye tsananguro zvinoonekwa paunenge uchitora bvunzo.

  1. Ndeapi matanho mana makuru eMonte Carlo Tree Search iteration?
    • Sarudzo, Kuwedzera, Simulation, Backpropagation
    • Ronga, sefa, batanidza, kubuda
    • Encode, decode, kupinda, kufanotaura
    • Rovedza, simbisa, edza, tumira
  2. Chii chinonzi UCT yekusarudza fomula chiyero?
    • Kumhanya kunopesana nendangariro
    • Kushandiswa kwehukoshi hwepamusoro-soro kunopesana nekuongorora kwevasina kushanyirwa
    • Kudzidziswa kunopesana nekufungidzira
    • Upamhi maringe nekudzika kwetiweki
  3. Muchinyakare MCTS, chii chinoitika panguva ye 'simulation' (kuburitsa) nhanho?
    • Muti unodzimwa
    • Iyo neural network inodzidziswa zvakare
    • Mutambo unotambwa kune mhedzisiro kubva kune itsva node
    • Vana vese vanowedzerwa kamwechete
  4. What roles did AlphaGo’s policy and value networks play in its MCTS search?
    • The policy network computed transitions and the value network chose the root child
    • Both networks performed random rollouts without evaluating positions
    • The value network proposed legal moves while the policy network estimated terminal rewards
    • The policy network guided move selection and the value network estimated position strength; AlphaGo also used a rollout policy
  5. Mushure mekudzokorora kwakawanda, MCTS inowanzosarudza sei mafambiro ekupedzisira ekutamba?
    • Mudzi wemwana ane shanyo dzakawanda
    • Kufamba nemubairo wepamusoro wekukurumidza
    • Mwana asina kurongeka
    • Iyo yakadzika node mumuti
  6. Chii chinoita kuti MuZero ionekwe kana ichienzaniswa neAlphaZero?
    • Iyo haishandisi neural network
    • Inodzidza muenzaniso wayo wezvakatipoteredza, saka inogona kuronga pasina kupiwa mitemo
    • Inongoridza Go
    • Inosiya kutsvaga kwemuti
  7. In the UCT selection score, what does the exploration term reward?
    • Only the child with the highest raw reward
    • Moves that have already been selected most often, regardless of value
    • Children with fewer visits relative to their parent, encouraging further sampling
    • A random rollout’s final board size
  8. Which MCTS iteration step adds a new child to the existing tree?
    • Selection
    • Simulation
    • Kudzokera kumashure
    • Expansion