Consider 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.
6406533044694.
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.
6406533044697.
Assertion is false but Reason is true.
A published solution is not available for this question yet.
Consider a reinforcement learning agent trying to balance a pole in a continuous environment.
The agent receives a reward of +1 for each time step the pole remains balanced and 0 otherwise.
Which of the following statements accurately describes the differences between Monte Carlo and
Temporal Difference (TD) learning in this scenario?
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Monte Carlo methods update the value function based on complete episodes,
while TD methods update after each step.
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TD methods are guaranteed to converge to the optimal policy, while Monte
Carlo methods may not converge.
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Monte Carlo methods are less sensitive to the choice of the discount factor
compared to TD methods.
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TD methods are more effective in environments with high variance and
stochasticity compared to Monte Carlo methods.
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None of these.
A published solution is not available for this question yet.
Consider a reinforcement learning agent learning to control a robotic arm in a simulated
environment. The agent receives a reward of +1 for successfully placing an object in target
location 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 using the action taken in the next
state, while Q-learning updates its action-value function using the maximum action-value across
all possible actions in the next state.
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SARSA is less sensitive to the choice of policy compared to Q-learning, making
it more robust in environments with frequent changes.
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SARSA converges more quickly than Q-learning in environments with high
reward variance.
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Q-learning is inherently more computationally efficient than SARSA due to
fewer updates required per episode.
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None of these.
A published solution is not available for this question yet.
In Q-learning, what is the impact of maximization bias on the algorithm’s performance, especially
in environments with noisy or stochastic rewards?
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Maximization bias can lead to an overestimation of action values, causing the
algorithm to favor suboptimal actions and potentially delaying convergence to the optimal policy.
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Maximization bias tends to enhance the algorithm’s performance by
consistently selecting actions with higher estimated values, leading to quicker convergence.
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Maximization bias may result in excessive exploration, allowing the algorithm
to discover better strategies in environments with highly variable rewards.
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Maximization bias typically has a minor effect on the performance of Q-
learning, as the exploration process naturally mitigates its influence over time.
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None of these.
A published solution is not available for this question yet.
Which of the following best captures the key difference between policy gradient methods and Q-
learning in reinforcement learning?
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Policy gradient methods directly optimize the policy by computing gradients of
the expected reward, while Q-learning indirectly improves the policy by learning a value function
that estimates the expected rewards for actions.
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Q-learning focuses on optimizing the policy parameters directly, while policy
gradient methods estimate the action-value function to guide policy improvement.
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Policy gradient methods require a model of the environment’s dynamics to
compute gradients, while Q-learning does not rely on any such model.
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Q-learning is used primarily for continuous action spaces, while policy
gradient methods are better suited for discrete action spaces.
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None of these.
A published solution is not available for this question yet.
Consider following assertion reason pair:
**Assertion:** A3C can provide better performance compared to A2C, provided the updates are small
enough.
**Reason:** In A2C, if a thread runs for a long time, other threads have to wait for it to finish.
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Assertion and Reason are both true and Reason is a correct explanation of
Assertion.
6406533044753.
Assertion and Reason are both true and Reason is not a correct explanation of
Assertion.
6406533044754.
Assertion is true but Reason is false.
6406533044755.
Assertion is false but Reason is true.
A published solution is not available for this question yet.
