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game_agent.py
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game_agent.py
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"""This file contains all the classes you must complete for this project.
You can use the test cases in agent_test.py to help during development, and
augment the test suite with your own test cases to further test your code.
You must test your agent's strength against a set of agents with known
relative strength using tournament.py and include the results in your report.
"""
import random
import logging
from collections import deque
from scorefunctions import custom_score
class Timeout(Exception):
"""Subclass base exception for code clarity."""
pass
class CustomPlayer:
"""Game-playing agent that chooses a move using your evaluation function
and a depth-limited minimax algorithm with alpha-beta pruning. You must
finish and test this player to make sure it properly uses minimax and
alpha-beta to return a good move before the search time limit expires.
Parameters
----------
search_depth : int (optional)
A strictly positive integer (i.e., 1, 2, 3,...) for the number of
layers in the game tree to explore for fixed-depth search. (i.e., a
depth of one (1) would only explore the immediate sucessors of the
current state.)
score_fn : callable (optional)
A function to use for heuristic evaluation of game states.
iterative : boolean (optional)
Flag indicating whether to perform fixed-depth search (False) or
iterative deepening search (True).
method : {'minimax', 'alphabeta'} (optional)
The name of the search method to use in get_move().
timeout : float (optional)
Time remaining (in milliseconds) when search is aborted. Should be a
positive value large enough to allow the function to return before the
timer expires.
"""
def __init__(self, search_depth=3, score_fn=custom_score,
iterative=True, method='minimax', timeout=10., quiessant_search=False):
self.search_depth = search_depth
self.iterative = iterative
self.score = score_fn
self.method = method
self.time_left = None
self.TIMER_THRESHOLD = timeout
self.quiessant_search = quiessant_search
# self.logger = logging.getLogger('customplayer')
def get_move(self, game, legal_moves, time_left):
"""Search for the best move from the available legal moves and return a
result before the time limit expires.
This function must perform iterative deepening if self.iterative=True,
and it must use the search method (minimax or alphabeta) corresponding
to the self.method value.
**********************************************************************
NOTE: If time_left < 0 when this function returns, the agent will
forfeit the game due to timeout. You must return _before_ the
timer reaches 0.
**********************************************************************
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)
Board coordinates corresponding to a legal move; may return
(-1, -1) if there are no available legal moves.
"""
self.time_left = time_left
options = game.get_legal_moves()
assert options == legal_moves, "Mismatched moves"
# Perform any required initializations, including selecting an initial
# move from the game board (i.e., an opening book), or returning
# immediately if there are no legal moves
score, move = None, random.choice(legal_moves) if len(legal_moves) > 0 else None
try:
# Iterative deepening with Quiessance search:
if self.iterative is True:
results = deque(maxlen=3)
for depth in range (self.search_depth, 25):
score, move = self.dosearch(game, depth)
results.append((score, move))
if self.quiessant_search is True:
if len(results) >=3 and all(x[1] == move for x in results):
break
elif score == float('-inf') or score == float ('inf'):
break
if self.time_left() < self.TIMER_THRESHOLD:
break
else:
score, move = self.dosearch(game, self.search_depth)
assert score is not None
if len (options) > 0:
assert not (move is None or move is (-1,-1)), "Move ({}, {}) for '{}/{}' cannot be None or (-1,-1) if options ({}) exist".format(move, score, self.method, self.score, options)
assert move in options, "Move ({}, {}) for '{}/{}' not from existing list of moves ({})".format(move, score, self.method, self.score, options)
except Timeout:
# Handle any actions required at timeout, if necessary
pass
# Return the best move from the last completed search
# (or iterative-deepening search iteration)
return move
def dosearch(self, game, depth):
if self.method == 'minimax':
mm_s, mm_m = self.minimax(game, depth)
return mm_s, mm_m
else: # alphabeta
ab_s, ab_m = self.alphabeta(game, depth)
return ab_s, ab_m
def minimax(self, game, depth, maximizing_player=True, tab='\t'):
"""Implement the minimax search algorithm as described in the lectures.
Parameters
----------
game : isolation.Board
An instance of the Isolation game `Board` class representing the
current game state
depth : int
Depth is an integer representing the maximum number of plies to
search in the game tree before aborting
maximizing_player : bool
Flag indicating whether the current search depth corresponds to a
maximizing layer (True) or a minimizing layer (False)
Returns
-------
float
The score for the current search branch
tuple(int, int)
The best move for the current branch; (-1, -1) for no legal moves
Notes
-----
(1) You MUST use the `self.score()` method for board evaluation
to pass the project unit tests; you cannot call any other
evaluation function directly.
"""
if self.time_left() < self.TIMER_THRESHOLD:
raise Timeout()
legal_moves = game.get_legal_moves(game.active_player)
if legal_moves is not None and len(legal_moves)>0:
if depth>0: # Recursive case:
if maximizing_player: # MAXIMIZING ply
score, move = None, None
for i,m in enumerate(legal_moves):
newscore, _ = self.minimax(game.forecast_move(m), depth-1, maximizing_player=not maximizing_player, tab=tab+'\t')
if score is None or newscore > score:
score, move = newscore, m
else: # MINIMIZING ply
score, move = None, None
for i,m in enumerate(legal_moves):
newscore, _ = self.minimax(game.forecast_move(m), depth-1, maximizing_player=not maximizing_player, tab=tab+'\t')
if score is None or newscore < score:
score, move = newscore, m
else: # Base case (depth==0)
score, move = self.score(game, self), None
else: # We are at a DEAD-END here
score, move = self.score(game, self), (-1, -1)
return score, move
def alphabeta(self, game, depth, alpha=float("-inf"), beta=float("inf"), maximizing_player=True, tab='\t'):
"""Implement minimax search with alpha-beta pruning as described in the
lectures.
Parameters
----------
game : isolation.Board
An instance of the Isolation game `Board` class representing the
current game state
depth : int
Depth is an integer representing the maximum number of plies to
search in the game tree before aborting
alpha : float
Alpha limits the lower bound of search on minimizing layers
beta : float
Beta limits the upper bound of search on maximizing layers
maximizing_player : bool
Flag indicating whether the current search depth corresponds to a
maximizing layer (True) or a minimizing layer (False)
Returns
-------
float
The score for the current search branch
tuple(int, int)
The best move for the current branch; (-1, -1) for no legal moves
Notes
-----
(1) You MUST use the `self.score()` method for board evaluation
to pass the project unit tests; you cannot call any other
evaluation function directly.
"""
if self.time_left() < self.TIMER_THRESHOLD:
raise Timeout()
floor = alpha
ceiling = beta
legal_moves = game.get_legal_moves(game.active_player)
if legal_moves is not None and len(legal_moves)>0:
if depth>0: # Recursive case:
if maximizing_player: # MAXIMIZING ply
score, move = None, None
for i,m in enumerate(legal_moves):
newscore, _ = self.alphabeta(game.forecast_move(m), depth-1, floor, ceiling, maximizing_player=not maximizing_player, tab=tab+'\t')
if score is None or newscore > score:
score, move = newscore, m
# Alphabeta bookkeeping:
if score > floor:
floor = score # Constrains children at the next (minimizing) layer to be above this value
if score >= ceiling: # No need to search any more if we've crossed the upper limit at this max layer already
break
else: # MINIMIZING ply
# print (tab + "MINIMIZING: (({})) {} < score < {} || Moves: {}".format(depth, floor, ceiling, legal_moves))
score, move = None, None
for i,m in enumerate(legal_moves):
newscore, _ = self.alphabeta(game.forecast_move(m), depth-1, floor, ceiling, maximizing_player=not maximizing_player, tab=tab+'\t')
if score is None or newscore < score:
score, move = newscore, m
# Alphabeta bookkeeping:
if score < ceiling:
ceiling = score # Constrains children at the next (maximizing) layer to be below this value
if score <= floor: # No need to search any more if we've crossed the lower limit at this min layer already
break
else: # Base case (depth==0)
score, move = self.score(game, self), None
else: # We are at a DEAD-END here
score, move = self.score(game, self), (-1, -1)
return score, move