Consider the following assertion reason pair:
**Assertion**: Reinforcement learning is a type of unsupervised learning algorithm as both don’t have
correct labels.
**Reason**: In unsupervised learning, a reward like quantity is not maximized.
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Assertion and Reason are both true and Reason is a correct explanation of
Assertion.
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Assertion and Reason are both true and Reason is not a correct explanation of
Assertion.
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Assertion is true but Reason is false.
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Assertion is false but Reason is true.
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Consider a reinforcement learning agent navigating a grid world environment. The agent receives
rewards of +1 for reaching the goal state and 0 otherwise. Which of the following statements
accurately describes the differences between Monte Carlo and TD learning in this scenario?
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Monte Carlo updates are unbiased estimators of the true value function, while
TD updates may introduce bias.
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TD updates are guaranteed to converge to the optimal value function, while
Monte Carlo updates may not converge.
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Monte Carlo updates require less memory and computational resources
compared to TD updates.
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TD updates are more robust to noise and stochasticity in the environment
compared to Monte Carlo updates.
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None of these
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Consider a reinforcement learning agent learning to navigate a grid world environment. The agent
receives rewards of +1 for reaching the goal state and 0 otherwise. Which of the following
statements accurately describes a difference between SARSA and Q-learning in this scenario?
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SARSA updates its action-value function based on the action actually taken in
the next state, while Q-learning updates its action-value function based on the maximum action-
value in the next state.
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SARSA is guaranteed to converge to the optimal policy under certain
conditions, while Q-learning may diverge or oscillate without additional modifications.
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SARSA is more computationally efficient than Q-learning, requiring fewer
updates to converge to the optimal policy.
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SARSA and Q-learning exhibit similar performance in terms of convergence
speed and solution quality in this scenario.
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None of these
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In Q-learning, how does maximization bias affect the performance of the algorithm in complex
environments?
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Maximization bias can lead to overestimation of action values, resulting in
suboptimal policies and slower convergence to the optimal policy.
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Maximization bias helps to accelerate learning by prioritizing actions with
higher estimated values, leading to faster convergence to the optimal policy.
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Maximization bias reduces the exploration-exploitation trade-off, resulting in
more exploratory behavior and improved generalization to unseen states.
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Maximization bias has minimal impact on the performance of Q-learning in
complex environments, as it tends to balance out over time through exploration.
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None of these
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Which of the following statements best describes the primary difference between policy gradient
methods and value function methods?
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Policy gradient methods directly optimize the value function, while value
function methods optimize the policy parameters.
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Value function methods approximate the policy by learning the value function
and deriving the policy from it, while policy gradient methods directly optimize the policy
parameters by computing gradients of expected rewards.
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Policy gradient methods approximate the value function and use it to optimize
the policy, while value function methods directly optimize the policy using gradients of the log-
probability of actions.
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Value function methods directly optimize the policy parameters, while policy
gradient methods focus on approximating the value function.
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None of these
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Which of the following statements are true with regards to Monte Carlo value approximation
methods?
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To evaluate a policy using these methods, a subset of trajectories in which all
states are encountered at least once are enough to update all state-values.
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Monte-Carlo value function approximation methods need knowledge of the
full model.
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Monte-Carlo methods update state-value estimates only at the end of an
episode.
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None of these
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