Checkpoint 3

I realized that even simple things take quite a bit of thought when it comes
to creating games using code. I had to really think about how this game works
in order to fully understand the lab.

notes.md

@@ -31,9 +31,15 @@ and it's your turn, which action would you take? Why?
 ---+---+---   ---+---+---   ---+---+---   ---+---+---
    |   |         |   | O       |   |         |   |   
 
+board #1: position 0, to block the O's from getting 3 in a row.
+board #2: position 6, to block the O's from getting 3 in a row.
+board #3: position 0, because I do not need to block O's and this will allow me to have 2 places to get 3 in a row.
+board #4: position 4, because I will be tring to get 3 in a row.
+
 ### Initial game state
 
 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? 
 
+currently there is no reward for the inital state because all the spots are blank and nobody has won yet. We could also be at a this state after the game resets. Either a player will gain a point by winning or nobody gets a point if there is no winner. 
 

play_ttt.py

@@ -3,7 +3,7 @@ from ttt.view import TTTView
 from ttt.player import TTTHumanPlayer, TTTComputerPlayer
 
 player0 = TTTHumanPlayer("Player 1")
-player1 = TTTHumanPlayer("Player 2")
+player1 = TTTComputerPlayer("Robot")
 game = TTTGame()
 view = TTTView(player0, player1)
 

ttt/player.py

@@ -1,5 +1,5 @@
 from click import Choice, prompt
-from strategy.random_strategy import RandomStrategy
+from strategy.lookahead_strategy import LookaheadStrategy
 from ttt.game import TTTGame
 import random
 
@@ -24,7 +24,7 @@ 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."