A reinforcement learning algorithm that learns the optimal policy for a given environment.

A supervised learning algorithm that learns the relationship between input and output data.

An unsupervised learning algorithm that learns the structure of data without any labels.

A generative learning algorithm that learns to generate new data from a given distribution.

To maximize the cumulative reward.

To minimize the cumulative loss.

To find the optimal policy.

To learn the structure of the environment.

A function that estimates the expected cumulative reward for taking a given action in a given state.

A function that estimates the expected loss for taking a given action in a given state.

A function that estimates the probability of taking a given action in a given state.

A function that estimates the value of a given state.

By using the Bellman equation.

By using the gradient descent algorithm.

By using the backpropagation algorithm.

By using the k-means algorithm.

A policy that always takes the action with the highest Q-value.

A policy that takes the action with the highest Q-value with probability 1 - epsilon and a random action with probability epsilon.

A policy that takes the action with the highest Q-value with probability epsilon and a random action with probability 1 - epsilon.

A policy that takes a random action.

The rate at which the Q-values are updated.

The rate at which the epsilon-greedy policy is updated.

The rate at which the environment is updated.

The rate at which the reward function is updated.

A factor that controls the importance of future rewards.

A factor that controls the importance of past rewards.

A factor that controls the importance of the current reward.

A factor that controls the importance of the next reward.

Robotics.

Game playing.

Finance.

Healthcare.

SARSA.

Q-Learning.

Deep Q-Learning.

Policy Gradient.

SARSA.

Double Q-Learning.

Dueling Q-Learning.

All of the above.

Q-Learning uses the Bellman equation to update the Q-values, while SARSA uses the TD error.

Q-Learning uses the epsilon-greedy policy, while SARSA uses the softmax policy.

Q-Learning uses a single Q-function, while SARSA uses two Q-functions.

Q-Learning is an off-policy algorithm, while SARSA is an on-policy algorithm.

Q-Learning uses a tabular representation of the environment, while Deep Q-Learning uses a neural network representation.

Q-Learning uses the epsilon-greedy policy, while Deep Q-Learning uses the softmax policy.

Q-Learning uses a single Q-function, while Deep Q-Learning uses two Q-functions.

Q-Learning is an off-policy algorithm, while Deep Q-Learning is an on-policy algorithm.

It is a model-free algorithm.

It can be used to solve a wide range of problems.

It is easy to implement.

All of the above.

It can be slow to converge.

It can be sensitive to the learning rate and the discount factor.

It can be difficult to explore the environment efficiently.

All of the above.