Reinforcement Learning Quiz

In reinforcement learning, what is the primary goal of an agent?

• To maximize its total reward over time

• To minimize the number of actions it takes

• To maximize the loss function

• To maintain a constant policy

Which of the following best describes the "exploration-exploitation trade-off"?

• Choosing between maximizing immediate rewards and achieving the optimal policy

• The trade-off between performing actions and observing rewards

• The balance between exploring new actions and exploiting known actions for reward

• The trade-off between the number of actions and the reward

What does a Markov Decision Process (MDP) consist of in reinforcement learning?

• States, actions, rewards, and transitions

• Layers, nodes, weights, and biases

• Inputs, outputs, and hidden layers

• Agents, networks, data, and training sets

Which of the following reinforcement learning algorithms is considered "model-free"?

• Dynamic Programming

• SARSA

• Value Iteration

• Monte Carlo Tree Search

In Q-learning, what does the "Q" stand for?

• Quality

• Query

• Queue

• Quantity

Which of the following statements about rewards in reinforcement learning is correct?

• Rewards are always positive and fixed.

• Rewards are delayed and come at the end of an episode.

• Rewards can be positive or negative, providing feedback for actions.

• Rewards are fixed values for each state only.

In the SARSA algorithm, what does the term SARSA stand for?

• State-Action-Reward-State-Action

• Strategy-Action-Reward-State-Advantage

• Success-Action-Reward-Sequence-Achievement

• Simultaneous-Action-Reward-Sequence-Agent

Which function is updated during the Q-learning algorithm to learn the best action for each state?

• Loss function

• Q-function

• Reward function

• Transition function

In reinforcement learning, the value function represents which of the following?

• The probability of an action given a state

• The cumulative expected reward for a state or state-action pair

• The immediate reward for an action taken in a given state

• The rate of change of rewards over time

Which of the following algorithms uses both value functions and policy functions in its implementation?

• Q-learning

• Actor-Critic

• Monte Carlo

• Dynamic Programming