RL Learn & Play Quiz

Froggy Jumps

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Fun, learn RL with feedback

Créé par

tisha agarwal

India

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RL Learn & Play Quiz


Froggy Jumps RL Learn & Play QuizVersion en ligne Fun, learn RL with feedback par tisha agarwal 1 What are the two core components of Reinforcement Learning? a Supervised and Unsupervised Learning b Policy and Value c Agent and Environment 2 What is the main goal of the agent in RL? a Maximize long-term cumulative reward b Minimize training time c Learn from labeled data only 3 In RL, what constitutes a full interaction between agent and environment? a State, Action, Reward, Next State b Input, Output, Loss c Data, Model, Prediction 4 How does RL differ from supervised or unsupervised learning? a Requires a pre-defined dataset b Uses labeled data only c Learn from environment feedback, without external supervision 5 What is the balance between exploring and exploiting in RL? a Always exploit first b Never explore new actions c Early exploration, later exploitation of learned actions 6 Which of these is NOT listed as a real-world RL application in the material? a Image compression b Games c Smart Vehicles 7 What is a policy in RL? a A mapping from each state to the best action b A dataset of states c A fixed sequence of moves 8 What does a reward represent in RL? a Ground truth label b Feedback from the environment about actions taken c Model accuracy score 9 What does convergence mean in RL? a Reaching zero loss b Learning a stable policy that maximizes expected reward c Stopping after first success 10 Which approach combination helped AlphaGo win Go, as mentioned? a Genetic Algorithms + Backpropagation b Monte Carlo Tree Search + Policy Gradients c Q-Learning + SARSA

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RL Learn & Play Quiz

Froggy Jumps

À propos de cette activité

Créé par

Téléchargez la version pour jouer sur papier

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RL Learn & Play QuizVersion en ligne

par tisha agarwal

What are the two core components of Reinforcement Learning?

What is the main goal of the agent in RL?

In RL, what constitutes a full interaction between agent and environment?

How does RL differ from supervised or unsupervised learning?

What is the balance between exploring and exploiting in RL?

Which of these is NOT listed as a real-world RL application in the material?

What is a policy in RL?

What does a reward represent in RL?

What does convergence mean in RL?

Which approach combination helped AlphaGo win Go, as mentioned?