def main(weights_name, video_name=None, get_image=False):
env = DQN.Environment(render=True, sigma=0.02, down=1.0, get_image=get_image)
s_size = env.env.s_size
agent = DQN.Agent(s_size=s_size)
agent.network.model.load_weights("data/" + weights_name + ".h5", by_name=True)
print("model loaded")
for _ in range(3):
s = time.time()
if video_name:
env.record("data/mov/" + video_name + ".mp4")
step = env.replay(agent.policy)
print("unicycle lasted {} steps and {:2f} seconds.".format(step, step/30))
print("time = {}".format(time.time() - s))
env.close()
def run():
training_intervals = 100
interval_interactions = 100
training_steps = training_intervals * interval_interactions
game_settings = GameSettings()
title = 'Convolutional DDQN + PER'
print(title)
plt.title(title)
agent = DQN.Agent(game_settings, training_steps)
interactions, scores, devs = agent.learning_curve(training_intervals,
interval_interactions)
plt.plot(interactions, smooth(scores), label='Conv. DDQN+PER')
# plt.fill_between(interactions, scores - devs, scores + devs, alpha=0.3)
plt.xlabel('Interactions')
plt.ylabel('Score')
plt.legend()
plt.show()
Return_test = Return[cutpoint:]
Price_test = Price[cutpoint:]
state_size = 7
lag = int((state_size - 3) / 2)
#ticker1=np.random.choice(return1.columns)
#ticker2=np.random.choice(return2.columns)
ticker1 = 'AEP'
ticker2 = 'AAPL'
environment = env.env(act, state_size, ticker1, ticker2, Return_train,
Price_train, 100)
optimizer = Adam(learning_rate=0.05)
agent = DQN.Agent(environment, optimizer)
##############################################################################
"""
Training model
"""
keras.backend.clear_session()
batch_size = 8
num_of_episodes = 10
timesteps_per_episode = len(Return_train)
agent.q_network.summary()
print(
"#######################################################################")
print("Constructing portfolio with", ticker1, 'and', ticker2)
#clear expirience storage
agent.store_clear()
import tensorflow as tf
import gym
import numpy as np
import random
import matplotlib.pyplot as plt
import DQN
env = gym.make('MountainCar-v0')
env.reset()
env._max_episodes = 3000
model = tf.keras.Sequential()
model.add(tf.keras.layers.Dense(24, input_shape=(4, ), activation='relu'))
model.add(tf.keras.layers.Dense(2, activation='linear'))
model.compile(loss='mse', optimizer=tf.keras.optimizers.Adam(lr=.001))
model = tf.keras.models.load_model("traditional.hd5")
episode = []
memory = []
agent = DQN.Agent(.95, .5, .999, model, env)
print(env.observation_space.shape[0])
agent.train(100, 10, 64)
agent.playGame()
model.save("model.hd5")
env.close()
def __init__(self):
self.agent = DQN.Agent(num_states=24, num_actions=9, memory_cap=1000)
self.num_states = 24
self.num_actions = 9
def reset(self):
self.agent = DQN.Agent(num_states=self.num_states, num_actions=self.num_actions, memory_cap=1000)
print("Load parameters...")
self.agent.brain.load_model('./test_weight.hdf5')
