Guide-yakabatana mibvunzo · Pakati Nhanho

Kutevedzera Kudzidza Quiz

Inotarisa maitiro ekuita cloning, kugovera shanduko, nyanzvi yekuratidzira, DAgger's data aggregation uye zvisingakwanisi kunze kwenyika dzakacherechedzwa.

Nzira dzekutungamira dzinoenderanaKutevedzera Kudzidza
Mubvunzo 1 ye 8

Mukudzidza kwekutevedzera, mumiririri anowana sei maitiro ayo?

Tarisa mubvunzo wese8 mibvunzo

Mhinduro uye tsananguro zvinoonekwa paunenge uchitora bvunzo.

  1. Mukudzidza kwekutevedzera, mumiririri anowana sei maitiro ayo?
    • Kudzidza mutemo nekukopa zviratidzo zvenyanzvi
    • Kukwirisa mubairo wakagadzirwa nemaoko kuburikidza nekuongorora zvisina tsarukano
    • Kudzvanya neural network kumhanya pamidziyo midiki
    • Kugadzira mifananidzo kubva kumashoko ekurudziro
  2. Behavioral cloning mafuremu ekutevedzera kudzidza serudzii rwedambudziko?
    • Kuungana kusingatarisirwi
    • Kutariswa kudzidza kwemamiriro-kune-chiito mepu
    • Chizvarwa chemhandu
    • Mubayiro kuumba
  3. Idambudziko ripi rinokonzeresa kuti hunhu hwekuita cloning hukundikane pamusoro pekutevedzana kwechiito kwenguva refu?
    • Overfitting kune basa remubairo
    • Kunyangarika magradients mune encoder
    • Kukanganisa kukanganisa kubva pakuchinja kwekugovera
    • Njodzi kukanganwa makirasi ekare
  4. Iyo DAgger algorithm inogadzirisa sei kushaya simba uku?
    • Nekubvisa nyanzvi kubva pachiuno zvachose
    • Nekuwedzera iyo discount factor
    • Nekuwedzera ruzha rusina kurongeka kune zvese zviito
    • Nekubvunza nyanzvi pamatunhu mudzidzi anoshanya nekuunganidza iyo data
  5. Sei kudzidza kutevedzera kuchiwanzo kufarirwa pane kusimbisa kudzidza kwemabasa akaita sekutyaira?
    • Kuratidzira kuri nyore kupa pane basa remubairo rakarurama
    • Inovimbisa maitiro akanaka
    • Hazvimbodi chero data
    • Inomhanya nekukurumidza pakufungidzira chete
  6. What is a realistic limitation of behavioral cloning when the agent reaches states outside the demonstrations?
    • It is guaranteed to improve beyond the expert
    • Its behavior is not guaranteed there because those states may be out of distribution
    • It can only produce random actions
    • It automatically queries an expert at every step
  7. In one DAgger iteration, what does the learner do before requesting expert labels?
    • Deletes all earlier demonstrations
    • Trains only on randomly generated labels
    • Rolls out its current policy and records the states it actually visits
    • Replaces the expert policy permanently
  8. After the expert labels the states visited by the current policy, what happens to that data?
    • It is used only as an evaluation metric
    • It replaces the original observation space
    • It is discarded after one gradient step
    • It is aggregated with previous labeled data for retraining