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sample_players.py
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sample_players.py
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"""This file contains a collection of player classes for comparison with your
own agent and example heuristic functions.
"""
from random import randint
def null_score(game, player):
"""This heuristic presumes no knowledge for non-terminal states, and
returns the same uninformative value for all other states.
Parameters
----------
game : `isolation.Board`
An instance of `isolation.Board` encoding the current state of the
game (e.g., player locations and blocked cells).
player : hashable
One of the objects registered by the game object as a valid player.
(i.e., `player` should be either game.__player_1__ or
game.__player_2__).
Returns
----------
float
The heuristic value of the current game state.
"""
if game.is_loser(player):
return float("-inf")
if game.is_winner(player):
return float("inf")
return 0.
def open_move_score(game, player):
"""The basic evaluation function described in lecture that outputs a score
equal to the number of moves open for your computer player on the board.
Parameters
----------
game : `isolation.Board`
An instance of `isolation.Board` encoding the current state of the
game (e.g., player locations and blocked cells).
player : hashable
One of the objects registered by the game object as a valid player.
(i.e., `player` should be either game.__player_1__ or
game.__player_2__).
Returns
----------
float
The heuristic value of the current game state
"""
if game.is_loser(player):
return float("-inf")
if game.is_winner(player):
return float("inf")
return float(len(game.get_legal_moves(player)))
def improved_score(game, player):
"""The "Improved" evaluation function discussed in lecture that outputs a
score equal to the difference in the number of moves available to the
two players.
Parameters
----------
game : `isolation.Board`
An instance of `isolation.Board` encoding the current state of the
game (e.g., player locations and blocked cells).
player : hashable
One of the objects registered by the game object as a valid player.
(i.e., `player` should be either game.__player_1__ or
game.__player_2__).
Returns
----------
float
The heuristic value of the current game state
"""
if game.is_loser(player):
return float("-inf")
if game.is_winner(player):
return float("inf")
own_moves = len(game.get_legal_moves(player))
opp_moves = len(game.get_legal_moves(game.get_opponent(player)))
return float(own_moves - opp_moves)
class RandomPlayer():
"""Player that chooses a move randomly."""
def get_move(self, game, legal_moves, time_left):
"""Randomly select a move from the available legal moves.
Parameters
----------
game : `isolation.Board`
An instance of `isolation.Board` encoding the current state of the
game (e.g., player locations and blocked cells).
legal_moves : list<(int, int)>
A list containing legal moves. Moves are encoded as tuples of pairs
of ints defining the next (row, col) for the agent to occupy.
time_left : callable
A function that returns the number of milliseconds left in the
current turn. Returning with any less than 0 ms remaining forfeits
the game.
Returns
----------
(int, int)
A randomly selected legal move; may return (-1, -1) if there are
no available legal moves.
"""
if not legal_moves:
return (-1, -1)
return legal_moves[randint(0, len(legal_moves) - 1)]
class GreedyPlayer():
"""Player that chooses next move to maximize heuristic score. This is
equivalent to a minimax search agent with a search depth of one.
"""
def __init__(self, score_fn=open_move_score):
self.score = score_fn
def get_move(self, game, legal_moves, time_left):
"""Select the move from the available legal moves with the highest
heuristic score.
Parameters
----------
game : `isolation.Board`
An instance of `isolation.Board` encoding the current state of the
game (e.g., player locations and blocked cells).
legal_moves : list<(int, int)>
A list containing legal moves. Moves are encoded as tuples of pairs
of ints defining the next (row, col) for the agent to occupy.
time_left : callable
A function that returns the number of milliseconds left in the
current turn. Returning with any less than 0 ms remaining forfeits
the game.
Returns
----------
(int, int)
The move in the legal moves list with the highest heuristic score
for the current game state; may return (-1, -1) if there are no
legal moves.
"""
if not legal_moves:
return (-1, -1)
_, move = max([(self.score(game.forecast_move(m), self), m) for m in legal_moves])
return move
class HumanPlayer():
"""Player that chooses a move according to user's input."""
def get_move(self, game, legal_moves, time_left):
"""
Select a move from the available legal moves based on user input at the
terminal.
**********************************************************************
NOTE: If testing with this player, remember to disable move timeout in
the call to `Board.play()`.
**********************************************************************
Parameters
----------
game : `isolation.Board`
An instance of `isolation.Board` encoding the current state of the
game (e.g., player locations and blocked cells).
legal_moves : list<(int, int)>
A list containing legal moves. Moves are encoded as tuples of pairs
of ints defining the next (row, col) for the agent to occupy.
time_left : callable
A function that returns the number of milliseconds left in the
current turn. Returning with any less than 0 ms remaining forfeits
the game.
Returns
----------
(int, int)
The move in the legal moves list selected by the user through the
terminal prompt; automatically return (-1, -1) if there are no
legal moves
"""
if not legal_moves:
return (-1, -1)
print(('\t'.join(['[%d] %s' % (i, str(move)) for i, move in enumerate(legal_moves)])))
valid_choice = False
while not valid_choice:
try:
index = int(input('Select move index:'))
valid_choice = 0 <= index < len(legal_moves)
if not valid_choice:
print('Illegal move! Try again.')
except ValueError:
print('Invalid index! Try again.')
return legal_moves[index]
if __name__ == "__main__":
from isolation import Board
# create an isolation board (by default 7x7)
player1 = RandomPlayer()
player2 = GreedyPlayer()
game = Board(player1, player2)
# place player 1 on the board at row 2, column 3, then place player 2 on
# the board at row 0, column 5; display the resulting board state. Note
# that .apply_move() changes the calling object
game.apply_move((2, 3))
game.apply_move((0, 5))
print(game.to_string())
# players take turns moving on the board, so player1 should be next to move
assert(player1 == game.active_player)
# get a list of the legal moves available to the active player
print(game.get_legal_moves())
# get a successor of the current state by making a copy of the board and
# applying a move. Notice that this does NOT change the calling object
# (unlike .apply_move()).
new_game = game.forecast_move((1, 1))
assert(new_game.to_string() != game.to_string())
print("\nOld state:\n{}".format(game.to_string()))
print("\nNew state:\n{}".format(new_game.to_string()))
# play the remainder of the game automatically -- outcome can be "illegal
# move" or "timeout"; it should _always_ be "illegal move" in this example
winner, history, outcome = game.play()
print("\nWinner: {}\nOutcome: {}".format(winner, outcome))
print(game.to_string())
print("Move history:\n{!s}".format(history))