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# Evaluation of Best Player
# ====================
# Import packages
from game import State, random_action, alpha_beta_action, mcts_action
from pv_mcts import pv_mcts_action
from tensorflow.keras.models import load_model
from tensorflow.keras import backend as K
from pathlib import Path
import numpy as np
# Prepare parameters
EP_GAME_COUNT = 10 # Number of games per evaluation
# Points for the first player
def first_player_point(ended_state):
# 1: first player wins, 0: first player loses, 0.5: draw
if ended_state.is_lose():
return 0 if ended_state.is_first_player() else 1
return 0.5
# Execute one game
def play(next_actions):
# Generate state
state = State()
# Loop until the game ends
while True:
# When the game ends
if state.is_done():
break
# Get action
next_action = next_actions[0] if state.is_first_player() else next_actions[1]
action = next_action(state)
# Get the next state
state = state.next(action)
# Return points for the first player
return first_player_point(state)
# Evaluation of any algorithm
def evaluate_algorithm_of(label, next_actions):
# Repeat multiple matches
total_point = 0
for i in range(EP_GAME_COUNT):
# Execute one game
if i % 2 == 0:
total_point += play(next_actions)
else:
total_point += 1 - play(list(reversed(next_actions)))
# Output
print('\rEvaluate {}/{}'.format(i + 1, EP_GAME_COUNT), end='')
print('')
# Calculate average points
average_point = total_point / EP_GAME_COUNT
print(label, average_point)
# Evaluation of the best player
def evaluate_best_player():
# Load the model of the best player
model = load_model('./model/best.keras')
# Generate a function to select actions using PV MCTS
next_pv_mcts_action = pv_mcts_action(model, 0.0)
# VS Random
next_actions = (next_pv_mcts_action, random_action)
evaluate_algorithm_of('VS_Random', next_actions)
# VS Alpha-Beta
next_actions = (next_pv_mcts_action, alpha_beta_action)
evaluate_algorithm_of('VS_AlphaBeta', next_actions)
# VS Monte Carlo Tree Search
next_actions = (next_pv_mcts_action, mcts_action)
evaluate_algorithm_of('VS_MCTS', next_actions)
# Clear model
K.clear_session()
del model
# Operation check
if __name__ == '__main__':
evaluate_best_player()
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