From 57400bd0ca2d9597dff5a9e295be1e59b04d61d1 Mon Sep 17 00:00:00 2001
From: Tanya-18 <71871242+Tanya-18@users.noreply.github.com>
Date: Fri, 8 Oct 2021 22:19:40 +0530
Subject: [PATCH] 02/02 requested changes made

---
 8-Reinforcement/README.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/8-Reinforcement/README.md b/8-Reinforcement/README.md
index f83f4051..29cb4a4f 100644
--- a/8-Reinforcement/README.md
+++ b/8-Reinforcement/README.md
@@ -37,7 +37,7 @@ Suppose you want to teach a computer to play a game, such as chess, or [Super Ma
 
 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:
 
-- **An environment** and **a simulator** which allows us to play a game many times. This simulator would define all the game rules as well as possible states and actions.
+- **An environment** and **a simulator** which allow us to play a game many times. This simulator would define all the game rules as well as possible states and actions.
 
 - **A reward function**, which would tell us how well we did during each move or game.