episode_total_reward, episode_avg_max_q, episode_duration
] #, episode_avg_loss]
summary_placeholders = [
tf.placeholder(tf.float32) for _ in range(len(summary_vars))
]
update_ops = [
summary_vars[i].assign(summary_placeholders[i])
for i in range(len(summary_vars))
]
summary_op = tf.summary.merge_all()
return summary_placeholders, update_ops, summary_op
if __name__ == "__main__":
# CartPole-v1 환경, 최대 타임스텝 수가 500
env = Env()
state_size = len(env.get_state())
action_size = env.action_size
# DQN 에이전트 생성
agent = DQNAgent(state_size, action_size)
scores, episodes, global_step = [], [], 0
for e in range(EPISODES):
print("---------------------------------------------------")
done = False
score, step = 0, 0
# env 초기화
state = env.reset()
state = np.reshape(state, [1, state_size])
from scipy import ndimage, misc
import image_segment
from environment import Env
from agent import R_Learner
import numpy as np
if __name__ == "__main__":
img = ndimage.imread('butterfly.jpg')
img = misc.imresize(img, size=0.0625)
search_env = img
patch_src = img
K = 50
labels = image_segment.segment(img, K)
label = 15
patch_indices = np.where(labels == label)
patch_pixels = img[patch_indices]
indices_at_origin = (patch_indices[0] - np.min(patch_indices[0]),
patch_indices[1] - np.min(patch_indices[1]))
env = Env(img)
agent = R_Learner(patch_pixels, indices_at_origin, env)
while True:
state = env.get_state()
action = agent.get_action(state)
reward, next_state = env.take_action(action)
agent.learn(state, action, reward, next_state)
