import numpy as np
import datetime
from catch_ball import CatchBall
from dqn_model import get_dqn_model
if __name__ == "__main__":
# environment, agent
env = CatchBall()
dqn = get_dqn_model(env)
try:
dqn.load_weights('dqn_{}_weights.h5f'.format("catch_ball"))
print("start from saved weights")
except:
print("start from random weights")
# Okay, now it's time to learn something! We visualize the training here for show, but this
# slows down training quite a lot. You can always safely abort the training prematurely using
# Ctrl + C.
dqn.fit(env, nb_steps=50000, visualize=False, verbose=1)
# After training is done, we save the final weights.
dqn.save_weights('dqn_{}_weights.h5f'.format("catch_ball"), overwrite=True)
dqn.save_weights('dqn_{}_weights.{}.h5f.bak'.format(
"catch_ball", datetime.datetime.today().strftime("%Y-%m%d-%H%M")
), overwrite=True)
# animate
img.set_array(state_t_1)
plt.axis("off")
return img,
if __name__ == "__main__":
# args
parser = argparse.ArgumentParser()
parser.add_argument("-m", "--model_path")
parser.add_argument("-s", "--save", dest="save", action="store_true")
parser.set_defaults(save=False)
args = parser.parse_args()
# environmet, agent
env = CatchBall()
agent = DQNAgent(env.enable_actions, env.name)
agent.load_model(args.model_path)
# variables
win, lose = 0, 0
state_t_1, reward_t, terminal = env.observe()
# animate
fig = plt.figure(figsize=(env.screen_n_rows / 2, env.screen_n_cols / 2))
fig.canvas.set_window_title("{}-{}".format(env.name, agent.name))
img = plt.imshow(state_t_1, interpolation="none", cmap="gray")
ani = animation.FuncAnimation(fig, animate, init_func=init, interval=(1000 / env.frame_rate), blit=True)
if args.save:
# save animation (requires ImageMagick)
import numpy as np
from catch_ball import CatchBall
from dqn_agent import DQNAgent
if __name__ == "__main__":
# parameters
n_epochs = 1000
# environment, agent
env = CatchBall()
agent = DQNAgent(env.enable_actions, env.name)
# variables
win = 0
for e in range(n_epochs):
# reset
frame = 0
loss = 0.0
Q_max = 0.0
env.reset()
state_t, reward_t_none = env.observe()
# execute action in environment
action_t = agent.select_action(state_t, agent.exploration)
env.execute_action(action_t)
# observe environment
state_t, reward_t = env.observe()
import numpy as np
from catch_ball import CatchBall
from dqn_agent import DQNAgent
if __name__ == "__main__":
# parameters
n_epochs = 1000
# environment, agent
env = CatchBall()
agent = DQNAgent(env.enable_actions, env.name)
# variables
win = 0
for e in range(n_epochs):
# reset
frame = 0
loss = 0.0
Q_max = 0.0
env.reset()
state_t_1, reward_t, terminal = env.observe()
while not terminal:
state_t = state_t_1
# execute action in environment
action_t = agent.select_action(state_t, agent.exploration)
env.execute_action(action_t)
plt.axis("off")
return img,
if __name__ == "__main__":
# args
parser = argparse.ArgumentParser()
parser.add_argument("-m", "--model_path")
parser.add_argument("-s", "--save", dest="save", action="store_true")
parser.add_argument("--simple", dest="is_simple", action="store_true", default=False,
help='Test simple model without cnn (8 x 8) (default: off)')
parser.set_defaults(save=False)
args = parser.parse_args()
# environmet, agent
env = CatchBall(time_limit=False, simple=args.is_simple)
agent = DQNAgent(env.enable_actions, env.name)
agent.load_model(args.model_path, simple=args.is_simple)
# variables
n_catched = 0
state_t_1, reward_t, terminal = env.observe()
S = deque(maxlen=state_num)
# animate
fig = plt.figure(figsize=(env.screen_n_rows / 2, env.screen_n_cols / 2))
fig.canvas.set_window_title("{}-{}".format(env.name, agent.name))
img = plt.imshow(state_t_1, interpolation="none", cmap="gray")
ani = animation.FuncAnimation(fig, animate, init_func=init, interval=(1000 / env.frame_rate), blit=True)
if args.save:
# animate
img.set_array(state_t_1)
plt.axis("off")
return img,
if __name__ == "__main__":
# args
parser = argparse.ArgumentParser()
parser.add_argument("-m", "--model_path")
parser.add_argument("-s", "--save", dest="save", action="store_true")
parser.set_defaults(save=False)
args = parser.parse_args()
# environmet, agent
env = CatchBall()
agent = DQNAgent(env.enable_actions, env.name)
agent.load_model(args.model_path)
# variables
win, lose = 0, 0
state_t_1, reward_t, terminal = env.observe()
balance = 0
# animate
fig = plt.figure(figsize=(env.screen_n_rows / 2, env.screen_n_cols / 2))
fig.canvas.set_window_title("{}-{}".format(env.name, agent.name))
img = plt.imshow(state_t_1, interpolation="none", cmap="gray")
ani = animation.FuncAnimation(fig,
animate,
init_func=init,
from __future__ import division
import argparse
import os
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
from catch_ball import CatchBall
from dqn_agent import DQNAgent
from debug_log import DebugLog
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument("-m", "--model_path")
args = parser.parse_args()
env = CatchBall()
agent = DQNAgent(env.enable_actions, env.name)
agent.load_model(args.model_path)
env.reset_board_status()
env.set_test_game()
# 1ゲーム内の処理開始地点
while env.is_playable() is True:
env.print_board()
env.test_player_play()
state = env.observe()
while True is True:
action = agent.select_action(state, 0.0)
import numpy as np
from catch_ball import CatchBall
from dqn_agent import DQNAgent
n_rows = 16
n_cols = 32
n_playerlength = 3
if __name__ == "__main__":
# parameters
n_epochs = 10000
# environment, agent
env = CatchBall(n_rows, n_cols, n_playerlength)
agent = DQNAgent(env.enable_actions, env.name, env.screen_n_rows, env.screen_n_cols, env.player_length)
# variables
win = 0
for e in range(n_epochs):
# reset
frame = 0
loss = 0.0
Q_max = 0.0
env.reset(keepPos=True)
state_t_1, reward_t, terminal = env.observe()
while not terminal:
state_t = state_t_1
from catch_ball import CatchBall
if __name__ == '__main__':
cb = CatchBall()
cb.run()
log.append(env.get_hand_name(challenger_hand))
if reward == 0:
print "AI EVEN " + "".join(log)
elif reward == 1:
print "AI WIN " + "".join(log)
else:
print "AI LOSE " + "".join(log)
if __name__ == "__main__":
# args
parser = argparse.ArgumentParser()
parser.add_argument("-m", "--model_path")
parser.add_argument("-a", "--action")
args = parser.parse_args()
# environmet, agent
env = CatchBall()
agent = DQNAgent(env.enable_actions, env.name)
agent.load_model(args.model_path)
env.set_card(args.action)
state_t, reward_t = env.observe()
# variables
action_t = agent.select_action(state_t, 0.0)
env.execute_action(action_t)
state_t, reward_t = env.observe()
print_result(env, int(action_t), env.get_hand_number(state_t), reward_t)
log = []
log.append("AI:")
log.append(env.get_hand_name(ai_hand))
log.append(" ")
log.append("Challenger:")
log.append(env.get_hand_name(challenger_hand))
if reward == 0:
print "AI EVEN " + "".join(log)
elif reward == 1:
print "AI WIN " + "".join(log)
else:
print "AI LOSE " + "".join(log)
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument("-m", "--model_path")
args = parser.parse_args()
env = CatchBall()
agent = DQNAgent(env.enable_actions, env.name)
agent.load_model(args.model_path)
env.reset()
env.set_question()
state_t_1, reward_t, terminal = env.observe()
action_t = agent.select_action(state_t_1, 0.0)
print state_t_1
print action_t
import numpy as np
from catch_ball import CatchBall
from dqn_agent import DQNAgent
if __name__ == "__main__":
# parameters
n_epochs = 1000
# environment, agent
env = CatchBall()
agent = DQNAgent(env.enable_actions, env.name)
# variables
win = 0
for e in range(n_epochs):
# reset
frame = 0
loss = 0.0
Q_max = 0.0
env.reset()
state_t_1, reward_t, terminal = env.observe()
while not terminal:
state_t = state_t_1
# execute action in environment
action_t = agent.select_action(state_t, agent.exploration)
env.execute_action(action_t)
#!/usr/bin/env python
# -*- coding: utf-8 -*-
import copy
import numpy as np
from catch_ball import CatchBall
from dqn_agent import DQNAgent
if __name__ == "__main__":
# 学習に回す回数
n_epochs = 0
n_game = 100
env = CatchBall()
agent = DQNAgent(env.enable_actions, env.name)
total_result_log = ""
for e in range(n_game):
# るーぷ開始地点
frame = 0
win = 0
loss = 0.0
Q_max = 0.0
env.reset_board_status()
env.set_new_game()
state_after = env.observe()
# 1ゲーム内の処理開始地点
while env.is_playable() is True:
# print "*********************************************************************************"
