## What affects convergence in Q-Learning?

In practice, a reinforcement learning algorithm is considered to converge when the learning curve gets flat and no longer increases. However, other elements should be taken into account since it depends on your use case and your setup. In theory, Q-Learning has been proven to converge towards the optimal solution.

**Is Approximate Q-Learning optimal?**

If Q-value estimates are correct a greedy policy is optimal. Instead of updating based on the best action from the next state, update based on the action your current policy actually takes from the next state.

**What is Perceptron convergence Theorem?**

Perceptron Convergence Theorem: For any finite set of linearly separable labeled examples, the Perceptron Learning Algorithm will halt after a finite number of iterations. In other words, after a finite number of iterations, the algorithm yields a vector w that classifies perfectly all the examples.

### How does Q learning find the optimal policy?

As we just saw, Q-learning finds the Optimal policy by learning the optimal Q-values for each state-action pair. Let’s look at the overall flow of the Q-Learning algorithm. Initially, the agent randomly picks actions. But as the agent interacts with the environment, it learns which actions are better, based on rewards that it obtains.

**Why does Q learning converge to the optimal value?**

If you think about it, it seems utterly incredible that an algorithm such as Q Learning converges to the Optimal Value at all. You start with arbitrary estimates, and then at each time-step, you update those estimates with other estimates. So why does this eventually give you better estimates?

**How is the Q value updated in reinforcement learning?**

Now the next state has become the new current state. The agent again uses the ε-greedy policy to pick an action. If it ends up exploring rather than exploiting, the action that it executes (a2) will be different from the target action (a4) used for the Q-value update in the previous time-step.

## How is Q learning explained step by step?

Develop intuition about why this algorithm converges to the optimal values.) Deep Q Networks (Our first deep-learning algorithm. A step-by-step walkthrough of exactly how it works, and why those architectural choices were made.) Policy Gradient (Our first policy-based deep-learning algorithm.)