pubeval evaluation
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README.org
10
README.org
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@ -24,8 +24,12 @@ command-line options and switches are listed by running =main.py= with the argum
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** Evaluation methods
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Currently, only a single evaluation method is implemented:
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Currently, the following evaluation methods are implemented:
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- =pubeval=: Evaluates against the =pubeval= backgammon benchmark developed by
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Gerald Tesauro. The source code is included in the =pubeval= directory and
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needs to be compiled before use. The binary should be placed at
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=pubeval/pubeval=.
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- =random=: Evaluates by playing against a player that makes random moves drawn
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from the set of legal moves. Should be used with high episode counts to lower
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variance. *TODO*: Doesn't even work currently
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@ -54,9 +58,9 @@ The following examples describe commmon operations.
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=python3 --eval --model-name=quack=
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*** Evaluate default model using evaluation methods =random= and =foovaluation=
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*** Evaluate default model using evaluation methods =random= and =pubeval=
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=python3 --eval --eval-methods random foovaluation=
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=python3 --eval --eval-methods random pubeval=
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* Model storage format
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4
bot.py
4
bot.py
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@ -48,9 +48,9 @@ class Bot:
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return random.choice(list(legal_moves))
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# TODO: Test this, the score results are deterministic
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# TODO: Test this, the score results should be deterministic
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def make_pubeval_move(self, board, sym, roll):
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legal_moves = Board.calculate_legal_states(board, sym, roll)
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legal_moves = Board.calculate_legal_states(tuple(board), sym, roll)
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moves_and_scores = []
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for board in legal_moves:
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call_argument = ["./pubeval/pubeval"]
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35
game.py
35
game.py
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@ -6,7 +6,7 @@ from cup import Cup
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import numpy as np
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import sys
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import time
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class Game:
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@ -131,10 +131,22 @@ class Game:
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def eval(self, trained_eps = 0):
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def do_eval(method, episodes = 1000, trained_eps = 0):
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start_time = time.time()
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def print_time_estimate(eps_completed):
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cur_time = time.time()
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time_diff = cur_time - start_time
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eps_per_sec = eps_completed / time_diff
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secs_per_ep = time_diff / eps_completed
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eps_remaining = (episodes - eps_completed)
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sys.stderr.write("[EVAL ] Averaging {per_sec} episodes per second\n".format(per_sec = round(eps_per_sec, 2)))
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sys.stderr.write("[EVAL ] {eps_remaining} episodes remaining; approx. {time_remaining} seconds remaining\n".format(eps_remaining = eps_remaining, time_remaining = int(eps_remaining * secs_per_ep)))
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sys.stderr.write("[EVAL ] Evaluating {eps} episode(s) with method '{method}'\n".format(eps=episodes, method=method))
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if method == 'random':
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outcomes = []
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for i in range(episodes):
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for i in range(1, episodes + 1):
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sys.stderr.write("[EVAL ] Episode {}".format(i))
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self.board = Board.initial_state
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while Board.outcome(self.board) is None:
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@ -145,10 +157,29 @@ class Game:
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sys.stderr.write("\t outcome {}".format(Board.outcome(self.board)[1]))
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outcomes.append(Board.outcome(self.board)[1])
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sys.stderr.write("\n")
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if i % 50 == 0:
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print_time_estimate(i)
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return outcomes
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elif method == 'pubeval':
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outcomes = []
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# Add the evaluation code for pubeval, the bot has a method make_pubeval_move(board, sym, roll), which can be used to get the best move according to pubeval
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for i in range(1, episodes + 1):
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sys.stderr.write("[EVAL ] Episode {}".format(i))
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self.board = Board.initial_state
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while Board.outcome(self.board) is None:
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roll = self.roll()
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self.board = (self.p1.make_move(self.board, self.p1.get_sym(), roll))[0]
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roll = self.roll()
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self.board = Board.flip(self.p2.make_pubeval_move(Board.board_features_to_pubeval(self.board, self.p2.get_sym()), self.p2.get_sym(), roll)[0][0:26])
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sys.stderr.write("\t outcome {}".format(Board.outcome(self.board)[1]))
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outcomes.append(Board.outcome(self.board)[1])
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sys.stderr.write("\n")
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if i % 10 == 0:
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print_time_estimate(i)
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return outcomes
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else:
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sys.stderr.write("[EVAL ] Evaluation method '{}' is not defined\n".format(method))
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return [0]
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