AML Reinforcement Learning & Decision Making

Question 1

In reinforcement learning, what does the agent seek to maximize over time?

Accepted Answer

Cumulative discounted reward

Answer

An RL agent maximizes cumulative discounted reward over time, balancing immediate and future gains according to the discount factor.

Question 2

Which of the following is NOT a standard component of a Markov Decision Process (MDP)?

Accepted Answer

Neural network architecture

Answer

An MDP is formally defined by states, actions, transition probabilities, rewards, and a discount factor — neural network architecture is an implementation choice, not part of the MDP definition.

Question 3

What effect does setting the discount factor (γ) close to 0 have on a reinforcement learning agent?

Accepted Answer

The agent becomes myopic, prioritizing only immediate rewards

Answer

A discount factor near 0 causes the agent to heavily discount future rewards, making it focus almost entirely on immediate rewards (myopic behavior).

Question 4

What is the key distinction between model-based and model-free reinforcement learning?

Accepted Answer

Model-based RL learns or uses an environment model for planning; model-free learns directly from interactions

Answer

Model-based RL builds or leverages an explicit model of environment dynamics for planning, while model-free RL learns policies or value functions directly from experience without modeling transitions.

Question 5

What is the exploration-exploitation dilemma in reinforcement learning?

Accepted Answer

Balancing trying new actions to gather information against using known high-reward actions

Answer

The exploration-exploitation dilemma involves balancing the need to explore unknown actions (to discover better rewards) against exploiting already-known high-reward actions.

(AML) Advanced Machine Learning Certification Practice Test

(AML) Advanced Machine Learning Certification Practice Test

AML Reinforcement Learning & Decision Making