generated from mwc/lab_tic_tac_toe
70fdcaac79
Checkpoint 3: I think that human cognition is pretty similar to the computer learning rules, but we don't often think about rules of cognition like we do with rules for computer thinking, and our understanding of the brain is probably not as complete as our understanding of computers. Writing a program like this is like starting with a brain that hasn't had any previous inputs, but it's difficult to imagine a brain that hasn't had any inputs. I also feel like memory is probably the most unique thing in human cognition compared to the computer analog, because memory formation is affected by emotion, which doesn't really have a computer analog yet? Also the effect of aging and injury?
61 lines
3.0 KiB
Markdown
61 lines
3.0 KiB
Markdown
# Tic Tac Toe notes
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## Checkpoint 1 Notes
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Which class is responsible for each of the following behaviors?
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For each, explain how the behavior is accomplished.
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### Checking to see whether the game is over
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Class TTTGame is responsible for checking to see whether the game is over.
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To check if the game is over, the is_over method is called with the state
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as the argument. The is_over method calls board_is_full and check_winner
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for both players. To check if the board is full, True is returned if "-"
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is in the board still. To check if either player is a winnner, the code isn't
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finished, but True should be returned if the board has three of that players
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symbols in a row. All of these methods are in the TTTGame class.
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### Determining which actions are available at a particular state
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Class TTTGame is responsible for determining which actions are available
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at a particular state. It returns a list of the index positions where there
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are empty spaces '-'.
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### Showing the board
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Class TTTView is responsible for showing the board. The methods it uses include
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print_board, get_action, format_row, and format_value. The TTTView class attributes
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are greeting, goodbye, divider, x_color, o_color, and option color.
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### Choosing which action to play on a turn
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Class TTTHumanPlayer is responsible for choosing which action to play on a turn.
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It gets possible actions (blank spaces)from class TTTGame method get_actions.
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Then, it converts each of the possible actions into a string and stores as a list
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in choices. Then, it converts input to the "> " prompt to integer and stores in
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action variable and returns action.
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## Checkpoint 2 Notes
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### TTT Strategy
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For each of the following board states, if you are playing as X
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and it's your turn, which action would you take? Why?
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| O | O | | O | X | X | O |
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---+---+--- ---+---+--- ---+---+--- ---+---+---
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X | X | | X | X | O | O | |
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---+---+--- ---+---+--- ---+---+--- ---+---+---
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| | | | O | | | |
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In the first state shown, I would put an X in spot 5 to get a winning combo.
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In the second state shown, I would put an X in spot 5 to prevent 0 from winning next turn.
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In the third state shown, I would put an X in spot 0 because it will make it possible to get a winning combo on X's next turn regardless of where O plays.
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In the fourth state shown, I would put an X in spot 4 to have more possible ways to win. Half of the winning combos contain spot 4.
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### Initial game state
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You can get the inital game state using game.get_initial_state().
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What is the current and future reward for this state? What does this mean?
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The current and future reward for state = {"board": ['-','O','O','X','X','-','-','-','-'], "player_x": True} is 1. This means that X will win. X can play spot 5 and win.
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The initial state is:
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{'board': ['-', '-', '-', '-', '-', '-', '-', '-', '-'], 'player_x': True}
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For the initial state, the current and future reward is 0. That means it is equally likely for either player to win or lose. |