Contents

## Is Q-learning a TD method?

Q-Learning is an off-policy algorithm based on the TD method.

### What is V’s in reinforcement learning?

The V function states what the expected overall value (not reward!) of a state s under the policy π is. The Q function states what the value of a state s and an action a under the policy π is.

#### What are Q values in Q-learning?

The ‘Q’ in Q-learning stands for quality. Quality here represents how useful a given action is in gaining some future reward.

**What does Q stand for in Q-learning?**

quality

The ‘q’ in q-learning stands for quality. Quality in this case represents how useful a given action is in gaining some future reward.

**Is SARSA better than Q-Learning?**

If your goal is to train an optimal agent in simulation, or in a low-cost and fast-iterating environment, then Q-learning is a good choice, due to the first point (learning optimal policy directly). If your agent learns online, and you care about rewards gained whilst learning, then SARSA may be a better choice.

## What is a good Q value?

This is the “q-value.” A p-value of 5% means that 5% of all tests will result in false positives. A q-value of 5% means that 5% of significant results will result in false positives. Q-values usually result in much smaller numbers of false positives, although this isn’t always the case..

### What are the major issue with Q-Learning?

A major limitation of Q-learning is that it is only works in environments with discrete and finite state and action spaces.

#### What is Q-Learning good for?

Q-learning is a model-free reinforcement learning algorithm to learn the value of an action in a particular state. It does not require a model of the environment (hence “model-free”), and it can handle problems with stochastic transitions and rewards without requiring adaptations.

**Which is the key factor in TD learning?**

In this form of TD learning, after every step value function is updated with the value of the next state and along the way reward obtained. This observed reward is the key factor that keeps the learning grounded and algorithm converges after a sufficient number of sampling (in the limit of infinity).

**What’s the difference between Q-learning and expected Sarsa?**

Given the next state, the Q-learning algorithm moves deterministically in the same direction while SARSA follows as per expectation, and accordingly, it is called Expected SARSA. Its backup diagram is shown below. Refer to the below diagram for the Expected SARSA algorithm written in two different ways.

## Which is the target policy in Q learning?

In the Q-learning, target policy is a greedy policy and behavior policy is the ε-greedy policy (this ensures exploration). Refer to the below diagram for the Q-learning algorithm written in two different ways.