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)
ani_path = os.path.join(
os.path.dirname(os.path.abspath(__file__)), "tmp", "demo-{}.gif".format(env.name))
ani.save(ani_path, writer="imagemagick", fps=env.frame_rate)
else:
# show animation
plt.show()
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)
# observe environment
state_t_1, reward_t, terminal = env.observe()
# store experience
agent.store_experience(state_t, action_t, reward_t, state_t_1,
terminal)
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)
hand_result = env.test_ai_play(action)
if hand_result == "ok":
break
elif hand_result == "ng":
x = int(action % 8)
y = int(action / 8)
DebugLog.error(str(action) + ":" + str(x) + ":" + str(y))
pass
elif hand_result == "pass":
break
else:
print "Hung up"
# 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()
# store experience
agent.store_experience(state_t, action_t, reward_t)
# experience replay
agent.experience_replay()
# for log
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 "*********************************************************************************"
state_before = copy.deepcopy(state_after)
# 自分の手がOKになるまでループ（置けないところに置く可能性がある為）
while True is True:
env.is_available()
# 手を選ばせる。盤面情報と手のブレ率（random)を与える
# hand_result = env.random_play()
action_t = agent.select_action(state_before, agent.exploration)
hand_result = env.learning_play(action_t)
