typos fixing

8-Reinforcement/README.md

@@ -22,14 +22,14 @@ In previous sections, you have seen two examples of machine learning problems:
 - **Supervised**, where we have datasets that suggest sample solutions to the problem we want to solve. [Classification](../4-Classification/README.md) and [regression](../2-Regression/README.md) are supervised learning tasks.
 - **Unsupervised**, in which we do not have labeled training data. The main example of unsupervised learning is [Clustering](../5-Clustering/README.md).
 
-In this section, we will introduce you to a new type of learning problems which do not require labeled training data. There are a several types of such problems:
+In this section, we will introduce you to a new type of learning problems that does not require labeled training data. There are several types of such problems:
 
 - **[Semi-supervised learning](https://wikipedia.org/wiki/Semi-supervised_learning)**, where we have a lot of unlabeled data that can be used to pre-train the model.
 - **[Reinforcement learning](https://wikipedia.org/wiki/Reinforcement_learning)**, in which an agent learns how to behave by performing experiments in some simulated environment.
 
 ### Example - computer game
 
-Suppose you want to teach computer to play a game, such as chess, or [Super Mario](https://wikipedia.org/wiki/Super_Mario). For the computer to play a game, we need it to predict which move to make in each of the game states. While this may seem like a classification problem, it is not - because we do not have a dataset with states and corresponding actions. While we may have some data like existing chess matches or recording of players playing Super Mario, it is likely that that data will not sufficiently cover a large enough number of possible states.
+Suppose you want to teach a computer to play a game, such as chess, or [Super Mario](https://wikipedia.org/wiki/Super_Mario). For the computer to play a game, we need it to predict which move to make in each of the game states. While this may seem like a classification problem, it is not - because we do not have a dataset with states and corresponding actions. While we may have some data like existing chess matches or recording of players playing Super Mario, it is likely that that data will not sufficiently cover a large enough number of possible states.
 
 Instead of looking for existing game data, **Reinforcement Learning** (RL) is based on the idea of *making the computer play* many times and observing the result. Thus, to apply Reinforcement Learning, we need two things: