Explore essential concepts of actor-critic algorithms in reinforcement learning, including structures, functions, and key terminology. This quiz helps reinforce your foundational knowledge of actor-critic methods and their practical applications.
In an actor-critic algorithm, what is the main role of the 'actor' component?
Explanation: The actor's primary function is to choose actions according to the current policy being learned. The critic estimates the value function, helping the actor improve. Storing transition histories is not a direct responsibility of the actor or critic, and updating the learning rate is a separate process in many algorithms. Only the actor actively selects actions during learning.
What does the 'critic' element of an actor-critic algorithm evaluate?
Explanation: The critic is designed to assess the value or expected return associated with actions taken by the policy, guiding the actor’s improvement. It does not directly evaluate exploration or exploitation ratios, nor does it monitor technical failures or measure policy convergence speed directly. Only the third option correctly describes the critic’s purpose.
In actor-critic algorithms, what does the advantage function represent?
Explanation: The advantage function quantifies how much better (or worse) an action is compared to the average value expected from a given state, helping refine action choices. Summing past rewards describes the return, not the advantage. The next action selection and counting steps are unrelated to advantage calculation.
Which technique do actor-critic algorithms primarily use to update the actor component?
Explanation: Policy gradients allow the actor to adjust the policy parameters in a direction that increases expected rewards. Supervised learning is not typically used in pure reinforcement learning settings. Genetic algorithms are a separate class of optimization techniques, and 'hard coding' rewards is not a method for actor updates.
Why are actor-critic algorithms generally considered more sample-efficient than pure policy gradient methods?
Explanation: The critic provides value estimates that help the actor learn with less variance, making the learning process more stable and sample-efficient. Actor-critic algorithms do use function approximation, are not fully supervised, and do not rely solely on random actions. Only the second option captures the reason for their efficiency.
What kind of learning do critics often use to estimate value functions in actor-critic algorithms?
Explanation: Critics commonly use temporal difference learning to update value estimates incrementally as new rewards arrive. Batch normalization and clustering are techniques unrelated to value estimation in actor-critic contexts. Unsupervised pretraining is not standard in critic learning.
During training, how does the actor typically choose which action to take in a given state?
Explanation: Usually, the actor samples actions based on probabilities defined by its current policy, promoting exploration. Always picking highest-reward actions would prevent learning from less-tried choices. Random or fixed sequences do not utilize policy-driven decision-making, which is fundamental to the actor’s operation.
What is a key advantage of using actor-critic algorithms for continuous action space environments?
Explanation: Actor-critic methods can generate actions from parameterized distributions, making them effective for tasks with continuous action spaces. A reward signal is still needed, and they do not restrict to integer actions or disregard feedback. Only the first option reflects their true advantage in such scenarios.
How does the critic assist the actor in improving its policy over time?
Explanation: The critic analyzes action results, providing feedback that helps the actor adjust its policy for better future performance. Changing states or environment settings, and altering episode lengths, are not critic responsibilities and do not facilitate direct policy improvement.
Which of the following best distinguishes actor-critic algorithms from value-based methods such as Q-learning?
Explanation: The main distinction is that actor-critic algorithms separately maintain a policy function (the actor) and a value estimator (the critic), while value-based methods focus on estimating value functions only. Both types can handle delayed rewards and complex tasks. Q-learning and actor-critic methods often utilize exploration strategies.