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train.py
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train.py
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from es import EvolutionStrategy
import numpy as np
from game import Game, play
from win import Window, GAME_SPEED
import gi
from gi.repository import Gtk, GLib, Gdk
from datetime import datetime
from os import path
import os
from draw_chart import draw_chart
es = EvolutionStrategy(fn=play, noisep=50, sigma=0.1, alpha=0.001, layer_sizes=[[4, 500], [500, 1]], input_size=4)
load = path.join(path.dirname(__file__), 'load.npy')
np.random.seed(0)
# if load.npy exists, load the parameters from it
if path.exists(load) and not os.environ.get('SKIP_LOAD'):
es.layers = np.load(load)
# show the game every n iterations
SHOW_EVERY = int(os.environ.get('SHOW_EVERY', 100))
# save the parameters every n iterations
SAVE_EVERY = int(os.environ.get('SAVE_EVERY', 100))
# number of steps
STEPS = int(os.environ.get('STEPS', 10000))
# an id for saving the parameters in a folder
run_id = str(datetime.now())
print("run {}".format(run_id))
os.mkdir(path.join(path.dirname(__file__), 'saves', run_id))
# this function is called when showing the game to user
def step(game, update):
win = Window(game)
GLib.timeout_add(GAME_SPEED, lambda: timeout_kill(win, game))
GLib.timeout_add(GAME_SPEED, update)
GLib.timeout_add(GAME_SPEED, win.update)
win.show_all()
Gtk.main()
# once the bird has lost, kill the window and stop Gtk loop
def timeout_kill(win, game):
if game.lost:
Gtk.main_quit()
win.destroy()
return False
return True
reward_scatter_x = []
reward_scatter_y = []
reward_line_x = list(range(STEPS))
reward_line_y = []
for i in range(STEPS):
print("{}: ".format(i), end='')
rewards = es.train()
m = np.mean(rewards)
reward_line_y.append(m)
for r in rewards:
reward_scatter_x.append(i)
reward_scatter_y.append(r)
if SHOW_EVERY and i % SHOW_EVERY == 0:
play(es.forward, step=step)
Gtk.main_quit()
print(' shown')
else:
score = play(es.forward)
print(' score: {:.2f}'.format(score))
if i % SAVE_EVERY == 0:
p = path.join(path.dirname(__file__), 'saves', run_id, 'save-{}'.format(i))
np.save(p, es.layers)
draw_chart(reward_scatter_x, reward_scatter_y, reward_line_x, reward_line_y)