Kediska Barashada Xoojinta Khadka Tooska ah

Jawaabaha iyo sharraxaadaha waxay soo baxayaan markaad qaadanayso kediska.

1. Maxaa qeexaya barashada xoojinta offline?
   • Barashada xog-ururin go'an iyada oo aan la is-dhexgal deegaan cusub lahayn
   • Waxbarasho oo keliya iyada oo loo marayo sahaminta tooska ah ee joogtada ah
   • Tababarka iyada oo aan wax calaamad abaalmarin ah lahayn
   • Inference ordaya iyada oo aan wax model ah
2. Waa maxay caqabada dhexe ee farsamada ee RL offline?
   • La'aanta wax xog ah
   • Isbedelka qaybinta iyo qiyaasta xad dhaafka ah ee ficilada qaybinta ka baxsan
   • Sahminta khadka tooska ah ee xad dhaafka ah
   • Halbeegyo aad u yar oo shabakada ah
3. What does D4RL provide to researchers evaluating offline reinforcement-learning algorithms?
   • A guarantee that an offline policy is safe to deploy
   • A new optimizer that removes the need for logged data
   • A benchmark suite of datasets and tasks for offline RL evaluation
   • A dataset containing only online interactions collected by each test policy
4. Sidee buu Conservative-ka Q-Learning (CQL) ula dagaallamo qiimaynta?
   • Adiga oo sare u qaadaya qiyamka falalka aan la arkin
   • Adigoo meesha ka saaraya abaal-marinta gabi ahaanba
   • Adiga oo wax badan ka baadhaya aduunka dhabta ah
   • Iyadoo la hagaajinayo qiyamka Q ee falalka qaybinta ka baxsan
5. Sidee ayuu Transformer-ku u habeeyaa khadka tooska ah ee RL?
   • Sida qaabaynta taxanaha ah ee saadaaliya falalka ku xidhan soo celinta la rabo
   • Sida ururrada dawladaha
   • Sida raadinta random saafi ah
   • Sida kala soocidda sawirka la kormeerayo
6. Waa maxay sababta aan caadiga ah ee RL algorithms si fudud loo isticmaali karin sidii- offline?
   • Ma laha shabakad neerfaha
   • Ma ururin karaan xogta sixitaanka si ay u hagaajiyaan qiimaynta qiimaha
   • Weligood abaal-marin ma isticmaalaan
   • Wax siyaasad ah uma baahna
7. How does BCQ reduce reliance on actions absent from an offline dataset?
   • It adds random actions to every training transition
   • It assumes every action has been observed equally often
   • It constrains candidate actions to stay near actions the dataset supports
   • It removes the action-value function and reward
8. What value-learning method is central to the cited Implicit Q-Learning paper?
   • Maximum-likelihood fitting of a language tokenizer
   • Uniform regression over actions sampled from a live environment
   • A supervised image-classification loss
   • Expectile regression over values associated with dataset actions