from game_of_life import GameOfLife, FocusArea
import numpy as np
import matplotlib.pyplot as plt
from neural_network import neural_net, decode, random_board
from gui import GameOfLifeBoard, Dim
width, height = 16, 16
focus_area = FocusArea(max_col=width, max_row=height)
brd = GameOfLife(FocusArea(width, height), random_board())
for i in range(height):
brd = brd.add((i, i))
brd = brd.add((height - i - 1, i))
brd = brd.add((i, height // 2))
brd = brd.add((i, height // 2 - 1))
brd = brd.add((width // 2, i))
brd = brd.add((width // 2 - 1, i))
GameOfLifeBoard.pixels_per_box = 10
GameOfLifeBoard.inter_frame_time = 250
class Mutator:
def __call__(self, brd):
action = neural_net.predict(np.array(brd.to_numpy_array()))[0]
return brd.add(decode(action))
nnet.load('q_nnet.be')
if __name__ == '__main__':
for i in range(number_of_epochs):
exp_rate = exploration_rate
board = GameOfLife(focus_area) #random_board())
board = MultiInputGol(board, 3)
for j in range(game_iterations):
# if len(board) == 0:
# continue
bad_board, next_board, action, r = board.next(), None, None, None
if np.random.rand() < exp_rate:
action = list(random_board(1))[0]
is_random = True
next_board = board.add(action).next()
r = reward(board, next_board, bad_board)
nnet.remember(board.to_numpy_array(), r, encode(action), next_board.to_numpy_array())
else:
brd, actions, n_brd = [board], [], []
for _ in range(board.max_cnt):
_, a = monte_carlo(brd[-1], 8)
actions.append(a)
action = decode(a)
n_brd.append(brd[-1].add(action).next(print_out=True))
brd.append(n_brd[-1])
next_board = n_brd[-1]
r = reward(board, next_board, bad_board)
rewards = np.geomspace(learning_rate ** -(board.max_cnt - 1), 1, board.max_cnt) * r
