def __init__(self, state_size, window_size, trend, skip):
self.state_size = state_size
self.window_size = window_size
self.half_window = window_size // 2
self.trend = trend
self.skip = skip
self.X = tf.placeholder(tf.float32, (None, self.state_size))
self.REWARDS = tf.placeholder(tf.float32, (None))
self.ACTIONS = tf.placeholder(tf.int32, (None))
feed_forward = tf.layers.dense(self.X,
self.LAYER_SIZE,
activation=tf.nn.relu)
self.logits = tf.layers.dense(feed_forward,
self.OUTPUT_SIZE,
activation=tf.nn.softmax)
input_y = tf.one_hot(self.ACTIONS, self.OUTPUT_SIZE)
loglike = tf.log((input_y * (input_y - self.logits) + (1 - input_y) *
(input_y + self.logits)) + 1)
rewards = tf.tile(tf.reshape(self.REWARDS, (-1, 1)),
[1, self.OUTPUT_SIZE])
self.cost = -tf.reduce_mean(loglike * (rewards + 1))
self.optimizer = tf.train.AdamOptimizer(
learning_rate=self.LEARNING_RATE).minimize(self.cost)
self.sess = tf.InteractiveSession()
self.sess.run(tf.global_variables_initializer())
def sinusoidal_position_encoding(inputs, mask, repr_dim):
T = tf.shape(inputs)[1]
pos = tf.reshape(tf.range(0.0, tf.to_float(T), dtype=tf.float32), [-1, 1])
i = np.arange(0, repr_dim, 2, np.float32)
denom = np.reshape(np.power(10000.0, i / repr_dim), [1, -1])
enc = tf.expand_dims(
tf.concat(
[tf.sin(pos / denom), tf.cos(pos / denom)], 1), 0)
return tf.tile(enc, [tf.shape(inputs)[0], 1, 1]) * tf.expand_dims(
tf.to_float(mask), -1)
def position_encoding(inputs):
T = tf.shape(inputs)[1]
repr_dim = inputs.get_shape()[-1].value
pos = tf.reshape(tf.range(0.0, tf.to_float(T), dtype=tf.float32), [-1, 1])
i = np.arange(0, repr_dim, 2, np.float32)
denom = np.reshape(np.power(10000.0, i / repr_dim), [1, -1])
enc = tf.expand_dims(
tf.concat(
[tf.sin(pos / denom), tf.cos(pos / denom)], 1), 0)
return tf.tile(enc, [tf.shape(inputs)[0], 1, 1])
def __init__(self, state_size, window_size, trend, skip):
self.state_size = state_size
self.window_size = window_size
self.half_window = window_size // 2
self.trend = trend
self.skip = skip
tf.reset_default_graph()
self.X = tf.placeholder(tf.float32, (None, self.state_size))
self.Y = tf.placeholder(tf.float32, (None, self.state_size))
self.ACTION = tf.placeholder(tf.float32, (None))
self.REWARD = tf.placeholder(tf.float32, (None))
self.batch_size = tf.shape(self.ACTION)[0]
with tf.variable_scope('curiosity_model'):
action = tf.reshape(self.ACTION, (-1,1))
state_action = tf.concat([self.X, action], axis=1)
save_state = tf.identity(self.Y)
feed = tf.layers.dense(state_action, 32, activation=tf.nn.relu)
self.curiosity_logits = tf.layers.dense(feed, self.state_size)
self.curiosity_cost = tf.reduce_sum(tf.square(save_state - self.curiosity_logits), axis=1)
self.curiosity_optimizer = tf.train.RMSPropOptimizer(self.LEARNING_RATE) .minimize(tf.reduce_mean(self.curiosity_cost))
total_reward = tf.add(self.curiosity_cost, self.REWARD)
with tf.variable_scope("q_model"):
with tf.variable_scope("eval_net"):
x_action = tf.layers.dense(self.X, 128, tf.nn.relu)
self.logits = tf.layers.dense(x_action, self.OUTPUT_SIZE)
with tf.variable_scope("target_net"):
y_action = tf.layers.dense(self.Y, 128, tf.nn.relu)
y_q = tf.layers.dense(y_action, self.OUTPUT_SIZE)
q_target = total_reward + self.GAMMA * tf.reduce_max(y_q, axis=1)
action = tf.cast(self.ACTION, tf.int32)
action_indices = tf.stack([tf.range(self.batch_size, dtype=tf.int32), action], axis=1)
q = tf.gather_nd(params=self.logits, indices=action_indices)
self.cost = tf.losses.mean_squared_error(labels=q_target, predictions=q)
self.optimizer = tf.train.RMSPropOptimizer(self.LEARNING_RATE).minimize(
self.cost, var_list=tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, "q_model/eval_net"))
t_params = tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES, scope='q_model/target_net')
e_params = tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES, scope='q_model/eval_net')
self.target_replace_op = [tf.assign(t, e) for t, e in zip(t_params, e_params)]
self.sess = tf.InteractiveSession()
self.sess.run(tf.global_variables_initializer())
def _linear(first_dim, second_dim, name, activation=None):
"""Returns a linear transformation of `inputs`, followed by a reshape."""
linear = snt.Linear(first_dim * second_dim, name=name)(inputs)
if activation is not None:
linear = activation(linear, name=name + '_activation')
return tf.reshape(linear, [-1, first_dim, second_dim])
def __init__(self, state_size, window_size, trend, skip):
self.state_size = state_size
self.window_size = window_size
self.half_window = window_size // 2
self.trend = trend
self.skip = skip
tf.reset_default_graph()
self.INITIAL_FEATURES = np.zeros((4, self.state_size))
self.X = tf.placeholder(tf.float32, (None, None, self.state_size))
self.Y = tf.placeholder(tf.float32, (None, None, self.state_size))
self.hidden_layer = tf.placeholder(tf.float32,
(None, 2 * self.LAYER_SIZE))
self.ACTION = tf.placeholder(tf.float32, (None))
self.REWARD = tf.placeholder(tf.float32, (None))
self.batch_size = tf.shape(self.ACTION)[0]
self.seq_len = tf.shape(self.X)[1]
with tf.variable_scope('curiosity_model'):
action = tf.reshape(self.ACTION, (-1, 1, 1))
repeat_action = tf.tile(action, [1, self.seq_len, 1])
state_action = tf.concat([self.X, repeat_action], axis=-1)
save_state = tf.identity(self.Y)
cell = tf.nn.rnn_cell.LSTMCell(self.LAYER_SIZE,
state_is_tuple=False)
self.rnn, last_state = tf.nn.dynamic_rnn(
inputs=state_action,
cell=cell,
dtype=tf.float32,
initial_state=self.hidden_layer)
self.curiosity_logits = tf.layers.dense(self.rnn[:, -1],
self.state_size)
self.curiosity_cost = tf.reduce_sum(
tf.square(save_state[:, -1] - self.curiosity_logits), axis=1)
self.curiosity_optimizer = tf.train.RMSPropOptimizer(
self.LEARNING_RATE).minimize(
tf.reduce_mean(self.curiosity_cost))
total_reward = tf.add(self.curiosity_cost, self.REWARD)
with tf.variable_scope("q_model"):
with tf.variable_scope("eval_net"):
cell = tf.nn.rnn_cell.LSTMCell(self.LAYER_SIZE,
state_is_tuple=False)
rnn, self.last_state = tf.nn.dynamic_rnn(
inputs=self.X,
cell=cell,
dtype=tf.float32,
initial_state=self.hidden_layer)
self.logits = tf.layers.dense(rnn[:, -1], self.OUTPUT_SIZE)
with tf.variable_scope("target_net"):
cell = tf.nn.rnn_cell.LSTMCell(self.LAYER_SIZE,
state_is_tuple=False)
rnn, last_state = tf.nn.dynamic_rnn(
inputs=self.Y,
cell=cell,
dtype=tf.float32,
initial_state=self.hidden_layer)
y_q = tf.layers.dense(rnn[:, -1], self.OUTPUT_SIZE)
q_target = total_reward + self.GAMMA * tf.reduce_max(y_q, axis=1)
action = tf.cast(self.ACTION, tf.int32)
action_indices = tf.stack(
[tf.range(self.batch_size, dtype=tf.int32), action], axis=1)
q = tf.gather_nd(params=self.logits, indices=action_indices)
self.cost = tf.losses.mean_squared_error(labels=q_target,
predictions=q)
self.optimizer = tf.train.RMSPropOptimizer(
self.LEARNING_RATE).minimize(
self.cost,
var_list=tf.get_collection(
tf.GraphKeys.TRAINABLE_VARIABLES, "q_model/eval_net"))
t_params = tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES,
scope='q_model/target_net')
e_params = tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES,
scope='q_model/eval_net')
self.target_replace_op = [
tf.assign(t, e) for t, e in zip(t_params, e_params)
]
self.sess = tf.InteractiveSession()
self.sess.run(tf.global_variables_initializer())