Completed checkpoint 3

What I changed
(I gave the computer player the look ahead strategy. This also required importing LookaheadStrategy in player.py.
I also answered the questions in notes.md.)

Why I changed it
(I was working on completing checkpoint 3. I realized I had to do the importing since otherwise it wouldn't let me play.)

Estimate for remaining time to finish assignment: [I am REALLY bad at these estimates. I mean in terms of actual work time, I'm probably not too far off. Fingers crossed, maybe another hour of actual work time?]

notes.md

@@ -37,4 +37,5 @@ For the first one, I would put an X on the rightmost cell in the middle row. Thi
 You can get the inital game state using game.get_initial_state(). 
 What is the current and future reward for this state? What does this mean? 
 
+The current and future reward for this state is 1. This means the state favors player X assuming both players continue using the same look ahead strategy.
 

play_ttt.py

@@ -2,8 +2,8 @@ from ttt.game import TTTGame
 from ttt.view import TTTView
 from ttt.player import TTTHumanPlayer, TTTComputerPlayer
 
-player0 = TTTHumanPlayer("Player 1")
-player1 = TTTHumanPlayer("Player 2")
+player0 = TTTHumanPlayer("Pleayer 1")
+player1 = TTTComputerPlayer("Robot 1")
 game = TTTGame()
 view = TTTView(player0, player1)
 

ttt/player.py

@@ -1,4 +1,5 @@
 from click import Choice, prompt
+from strategy.lookahead_strategy import LookaheadStrategy
 from strategy.random_strategy import RandomStrategy
 from ttt.game import TTTGame
 import random
@@ -24,7 +25,7 @@ class TTTComputerPlayer:
     def __init__(self, name):
         "Sets up the player."
         self.name = name
-        self.strategy = RandomStrategy(TTTGame())
+        self.strategy = LookaheadStrategy(TTTGame(),deterministic=False)
 
     def choose_action(self, state):
         "Chooses a random move from the moves available."