Artificial Intelligence & Machine Learning

Reinforcement Learning Environment Mechanics

Focus on how agents perceive and act within environments, evaluating algorithm behavior and design choices.

#reward shaping#policy gradients#agent learning#value functions#environment dynamics

Total Questions

Difficulty Level

medium

Progress

0/0

Quiz Questions

Answer all questions below and test your knowledge.

1
In a high-dimensional continuous action space, such as robotic control, which of the following methods is most directly suited for learning a deterministic policy?
Question 1
2
A researcher observes that an agent converges to a suboptimal policy because it consistently chooses the first rewarding action it discovers, ignoring potentially better alternatives discovered later. This is a classic symptom of what challenge?
Question 2
3
When using function approximation (like a neural network) to represent a Q-function, what phenomenon can cause the estimated values to diverge or become unstable during training?
Question 3
4
In a partially observable Markov decision process (POMDP), the agent does not have direct access to the true environment state. Which of these is a common architectural approach to handle this partial observability?
Question 4
5
What is the primary purpose of using a separate target network in algorithms like DQN?
Question 5

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