Idanwo ti o sopọ mọ itọsọna · Alabọde Ipele

Monte Carlo Tree Search adanwo

Checks MCTS iteration stages, UCT exploration, rollout simulation, root-move choice and how AlphaGo combined search with networks.

Jẹmọ itọsọna awọn ọnaMonte Carlo Tree Search
Ibeere 1 ti 8

Kini awọn igbesẹ akọkọ mẹrin ti aṣetunṣe Igi Monte Carlo kan?

Awotẹlẹ gbogbo ibeere8 ibeere

Awọn idahun ati awọn alaye han bi o ṣe n mu ibeere naa.

  1. Kini awọn igbesẹ akọkọ mẹrin ti aṣetunṣe Igi Monte Carlo kan?
    • Aṣayan, Imugboroosi, Simulation, Backpropagation
    • Too, àlẹmọ, dapọ, jade
    • Fi koodu koodu, iyipada, lọ, sọtẹlẹ
    • Reluwe, sooto, idanwo, ran awọn
  2. Kini iwọntunwọnsi agbekalẹ yiyan UCT?
    • Iyara dipo iranti
    • Iwa ilokulo ti awọn gbigbe iye-giga dipo iṣawakiri ti awọn ti a ṣe abẹwo si
    • Ikẹkọ dipo itọkasi
    • Iwọn dipo ijinle nẹtiwọki
  3. Ni Ayebaye MCTS, kini o ṣẹlẹ lakoko igbesẹ 'kikopa' (yilọ)?
    • Igi naa ti paarẹ
    • Nẹtiwọọki nkankikan ti ni atunṣe
    • Awọn ere ti wa ni dun jade si ohun abajade lati titun ipade
    • Gbogbo awọn ọmọ ti wa ni ti fẹ ni ẹẹkan
  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. Lẹhin ọpọlọpọ awọn iterations, bawo ni MCTS ṣe maa n gbe gbigbe ikẹhin lati ṣere?
    • Awọn root ọmọ pẹlu awọn julọ ọdọọdun
    • Awọn Gbe pẹlu ga lẹsẹkẹsẹ ere
    • A ID ọmọ
    • Ipilẹ ti o jinlẹ julọ ninu igi naa
  6. Kini o jẹ ki MuZero ṣe akiyesi ni akawe si AlphaZero?
    • Ko lo awọn nẹtiwọọki nkankikan
    • O kọ ẹkọ awoṣe ti ara rẹ ti ayika, nitorina o le gbero laisi fifun awọn ofin
    • O ṣiṣẹ Go nikan
    • O kọ wiwa igi silẹ
  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
    • Itankalẹ afẹyinti
    • Expansion