from RL_brain import DoubleDQN
import numpy as np
import matplotlib.pyplot as plt
import tensorflow as tf
import matplotlib
from Res_plot import Res_plot
env = RIS_UAV()
res = Res_plot(env)
MEMORY_SIZE = 3200
Episodes = env.eps
sess = tf.Session()
with tf.variable_scope('Double_DQN_UAV'):
double_DQN_UAV = DoubleDQN(
n_actions=env.n_actions, n_features=env.n_features, memory_size=MEMORY_SIZE,
e_greedy_increment=0.001, double_q=True, ris=False, passive_shift=False, sess=sess, output_graph=True)
with tf.variable_scope('Double_DQN_FIX_RIS'):
double_DQN_RIS_Fix = DoubleDQN(
n_actions=env.n_actions, n_features=env.n_features, memory_size=MEMORY_SIZE,
e_greedy_increment=0.001, double_q=True, ris=True, passive_shift=False, sess=sess, output_graph=True)
with tf.variable_scope('Double_DQN_RIS'):
double_DQN_RIS = DoubleDQN(
n_actions=env.n_actions, n_features=env.n_features, memory_size=MEMORY_SIZE,
e_greedy_increment=0.001, double_q=True, ris=True, passive_shift=True, sess=sess, output_graph=True)
sess.run(tf.global_variables_initializer())
# record the results
action = RL.choose_action(observation)
observation_,reward,done = env.step(action)
RL.store_transition(observation, action, reward, observation_)
if (step > 200) and (step % 5 == 0):
RL.learn()
# swap observation
observation = observation_
# break while loop when end of this episode
if done:
break
step += 1
print('game over')
env.destroy()
if __name__ == '__main__':
env = Maze()
RL = DoubleDQN(env.n_actions, env.n_features,
learning_rate=0.01,
reward_decay=0.9,
e_greedy=0.9,
replace_target_iter=200,
memory_size=2000,
# output_graph=True
)
env.after(100, run_maze)
env.mainloop()
RL.plot_cost()
file=xlwt.Workbook(encoding='utf-8',style_compression=0)
sheet=file.add_sheet('aa')
t0= time.clock()
MyEngine = MyEngine()
MyEngine.QD_start()
MEMORY_SIZE = 500 #原值为3000
ACTION_SPACE = 3
sess = tf.Session()
RL = DoubleDQN(
n_actions=ACTION_SPACE, n_features=2, memory_size=MEMORY_SIZE,
e_greedy_increment=0.001, double_q=True, sess=sess, output_graph=True)
sess.run(tf.global_variables_initializer())
RL.load_net()
nk = 0
step = 0
cont = 1 # 存储数据用计数器
array = np.array([0], float)
observation_S = array
from RL_brain import DoubleDQN
import numpy as np
import matplotlib.pyplot as plt
import tensorflow as tf
env = gym.make('Pendulum-v0')
env = env.unwrapped
env.seed(1)
MEMORY_SIZE = 3000
ACTION_SPACE = 11
sess = tf.Session()
with tf.variable_scope('Natural_DQN'):
natural_DQN = DoubleDQN(n_actions=ACTION_SPACE,
n_features=3,
memory_size=MEMORY_SIZE,
e_greedy_increment=0.001,
double_q=False,
sess=sess)
with tf.variable_scope('Double_DQN'):
double_DQN = DoubleDQN(n_actions=ACTION_SPACE,
n_features=3,
memory_size=MEMORY_SIZE,
e_greedy_increment=0.001,
double_q=True,
sess=sess,
output_graph=True)
sess.run(tf.global_variables_initializer())
import gym
from RL_brain import DoubleDQN
env = gym.make('MountainCar-v0')
env = env.unwrapped
print(env.action_space,
env.observation_space,
env.observation_space.high,
env.observation_space.low,
sep='\n')
RL = DoubleDQN(n_actions=env.action_space.n,
n_features=env.observation_space.shape[0],
learning_rate=0.01,
e_greedy=0.9,
replace_target_iter=100,
memory_size=2000,
e_greedy_increment=0.001)
total_steps = 0
for i_episode in range(100):
observation = env.reset()
ep_r = 0
while True:
env.render()
action = RL.choose_action(observation)
observation_, reward, done, info = env.step(action)
if total_steps > MEMORY_SIZE:
RL.learn()
if total_steps - MEMORY_SIZE > 20000:
break
observation = observation_
total_steps += 1
return RL.q
if __name__ == "__main__":
DDQN = DoubleDQN(n_actions=ACTION_SPACE,
n_features=3,
learning_rate=0.01,
e_greedy=0.9,
replace_target_iter=100,
memory_size=2000,
e_greedy_increment=0.001,
double_q=True)
q_double = train(DDQN)
DQN = DoubleDQN(n_actions=ACTION_SPACE,
n_features=3,
learning_rate=0.01,
e_greedy=0.9,
replace_target_iter=100,
memory_size=2000,
e_greedy_increment=0.001,
double_q=False)
q_nature = train(DQN)
plt.plot(np.array(q_nature), c='r', label='natural')
plt.plot(np.array(q_double), c='b', label='double')
import gym
from RL_brain import DoubleDQN
import numpy as np
import matplotlib.pyplot as plt
env = gym.make("Pendulum-v0")
env = env.unwrapped
env.seed(1)
MEMORY_SIZE = 3000
ACTION_SPACE = 11
naturel_DQN = DoubleDQN(n_actions=ACTION_SPACE,
n_features=3,
memory_size=MEMORY_SIZE,
e_greedy_increment=0.001,
double_q=False)
double_DQN = DoubleDQN(n_actions=ACTION_SPACE,
n_features=3,
memory_size=MEMORY_SIZE,
e_greedy_increment=0.001,
double_q=True)
def train(RL):
total_steps = 0
observation = env.reset()
while True:
if total_steps - MEMORY_SIZE > 8000:
env.render() # show the game when trained for some time
action = RL.choose_action(observation)
f_action = (action - (ACTION_SPACE - 1) / 2) / (
env = env.unwrapped
env.seed(21)
MEMORY_SIZE = 10000
ACTION_SPACE = 3
FEATURE_SIZE = 2
print(env.action_space,
env.observation_space,
env.observation_space.high,
env.observation_space.low,
sep='\n')
sess = tf.Session()
with tf.variable_scope('Natural_DQN'):
natural_DQN = DoubleDQN(n_actions=ACTION_SPACE,
n_features=FEATURE_SIZE,
memory_size=MEMORY_SIZE,
e_greedy_increment=0.001,
double_q=False,
sess=sess)
with tf.variable_scope('Prioritized_DQN'):
prioritized_DQN = DQNPrioritizedReplay(n_actions=ACTION_SPACE,
n_features=FEATURE_SIZE,
memory_size=MEMORY_SIZE,
e_greedy_increment=0.001,
prioritized=True,
sess=sess,
output_graph=True)
sess.run(tf.global_variables_initializer())
episodes.append(episode)
break
step += 1 # 总步数
# end of game
print('game over')
env.destroy()
if __name__ == "__main__":
env = Maze()
RL = DoubleDQN(env.n_actions,
env.n_features,#observation/state 的属性，如长宽高
learning_rate=0.01,
reward_decay=0.9,
dueling=True,
e_greedy=0.9,
replace_target_iter=200, # 每 200 步替换一次 target_net 的参数
memory_size=2000, # 记忆上限
# output_graph=True # 是否输出 tensorboard 文件
)
env.after(100, run_maze)#进行强化学习训练
env.mainloop()
# 观看训练时间曲线
his_double = np.vstack((episodes, steps))
file = open('his_dueling_DDQN.pickle', 'wb')
pickle.dump(his_double, file)
file.close()
plt.plot(his_double[0, :], his_double[1, :] - his_double[1, 0], c='b', label='Dueling DDQN')
from RL_brain import DoubleDQN
import numpy as np
import matplotlib.pyplot as plt
import tensorflow as tf
env = gym.make('Pendulum-v0')
env = env.unwrapped
env.seed(1)
MEMORY_SIZE = 3000
ACTION_SPACE = 11
sess = tf.Session()
with tf.variable_scope('Natural_DQN'):
natural_DQN = DoubleDQN(
n_actions=ACTION_SPACE, n_features=3, memory_size=MEMORY_SIZE,
e_greedy_increment=0.001, double_q=True, sess=sess
)
with tf.variable_scope('Double_DQN'):
double_DQN = DoubleDQN(
n_actions=ACTION_SPACE, n_features=3, memory_size=MEMORY_SIZE,
e_greedy_increment=0.001, double_q=True, sess=sess, output_graph=True)
sess.run(tf.global_variables_initializer())
total_steps = 0
all_rewards = []
for i in range(400):
s_c = env.reset()
done = False
step = 0
reward = 0
if (step > 200) and (step % 5 == 0):
RL.learn()
# swap observation
observation = observation_
# break while loop when end of this episode
if done:
break
step += 1
print('game over')
env.destroy()
if __name__ == '__main__':
env = Maze()
RL = DoubleDQN(
env.n_actions,
env.n_features,
learning_rate=0.01,
reward_decay=0.9,
e_greedy=0.9,
replace_target_iter=200,
memory_size=2000,
# output_graph=True
)
env.after(100, run_maze)
env.mainloop()
RL.plot_cost()
