def build_dqn(self, params):
with tf.variable_scope(PREDICTION):
self.s_t = tf.placeholder(
dtype=tf.float32,
shape=[None, self.replay_memory.history_length,
self.replay_memory.num_channels],
name=HISTORICAL_PRICES
)
self.trade_rem_t = tf.placeholder(
dtype=tf.float32,
shape=[None,],
name=TRADE_REM
)
with tf.variable_scope(DROPOUT_KEEP_PROBS):
self.q_conv_keep_prob = tf.placeholder(tf.float32)
self.q_dense_keep_prob = tf.placeholder(tf.float32)
self.q_gru_keep_prob = tf.placeholder(tf.float32)
params.dropoutkeepprobs = DropoutKeepProbs(
self.q_conv_keep_prob,
self.q_dense_keep_prob,
self.q_gru_keep_prob
)
self.q = DeepSense(params, self.logger, self.sess, self.config, name=Q_NETWORK)
self.q.build_model((self.s_t, self.trade_rem_t))
with tf.variable_scope(TARGET):
self.t_s_t = tf.placeholder(
dtype=tf.float32,
shape=[None, self.replay_memory.history_length,
self.replay_memory.num_channels],
name=HISTORICAL_PRICES
)
self.t_trade_rem_t = tf.placeholder(
dtype=tf.float32,
shape=[None,],
name=TRADE_REM
)
params.dropoutkeepprobs = DropoutKeepProbs()
self.t_q = DeepSense(params, self.logger, self.sess, self.config, name=T_Q_NETWORK)
self.t_q.build_model((self.t_s_t, self.t_trade_rem_t))
with tf.variable_scope(UPDATE_TARGET_NETWORK):
self.q_weights_placeholders = {}
self.t_weights_assign_ops = {}
for name in self.q.weights.keys():
self.q_weights_placeholders[name] = tf.placeholder(
tf.float32,
self.q.weights[name].get_shape().as_list()
)
for name in self.q.weights.keys():
self.t_weights_assign_ops[name] = self.t_q.weights[name].assign(
self.q_weights_placeholders[name]
)
with tf.variable_scope(TRAINING):
self.target_q = tf.placeholder(tf.float32, [None], name=TARGET_Q)
self.action = tf.placeholder(tf.int64, [None], name=ACTION)
action_one_hot = tf.one_hot(self.action, self.config[NUM_ACTIONS],
1.0, 0.0, name=ACTION_ONE_HOT)
q_acted = tf.reduce_sum(self.q.values * action_one_hot,
reduction_indices=1, name=Q_ACTED)
with tf.variable_scope(LOSS):
self.delta = self.target_q - q_acted
self.global_step = tf.Variable(0, trainable=False)
self.loss = tf.reduce_mean(clipped_error(self.delta), name=LOSS)
with tf.variable_scope(OPTIMIZER):
self.learning_rate_step = tf.placeholder(tf.int64, None, name=LEARNING_RATE_STEP)
self.learning_rate_op = tf.maximum(self.learning_rate_minimum,
tf.train.exponential_decay(
self.learning_rate,
self.learning_rate_step,
self.learning_rate_decay_step,
self.learning_rate_decay,
staircase=True))
self.optimizer = tf.train.RMSPropOptimizer(
self.learning_rate_op, momentum=0.95, epsilon=0.01).minimize(self.loss)
with tf.variable_scope(SUMMARY):
scalar_summary_tags = ['average.reward', 'average.loss', 'average.q', \
'episode.max reward', 'episode.min reward', 'episode.avg reward', \
'episode.num of episodes', 'training.learning_rate']
self.summary_placeholders = {}
self.summary_ops = {}
for tag in scalar_summary_tags:
self.summary_placeholders[tag] = \
tf.placeholder('float32', None, name=tag.replace(' ', '_'))
self.summary_ops[tag] = \
tf.summary.scalar(
name="{}-{}".format(self.env_name, tag.replace(' ', '_')),
tensor=self.summary_placeholders[tag]
)
histogram_summary_tags = ['episode.rewards', 'episode.actions']
for tag in histogram_summary_tags:
self.summary_placeholders[tag] = \
tf.placeholder('float32', None, name=tag)
self.summary_ops[tag] = \
tf.summary.histogram(
tag,
self.summary_placeholders[tag]
)
self.sess.run(tf.local_variables_initializer())
self.sess.run(tf.global_variables_initializer())
self._saver = tf.train.Saver(self.q.weights.values() + [self.step_op], max_to_keep=30)
self.load_model()
self.update_target_network()
self._summary_writer = tf.summary.FileWriter(self.config[TENSORBOARD_LOG_DIR])
self._summary_writer.add_graph(self.sess.graph)
def build_dqn(self, params):
with tf.variable_scope(PREDICTION):
self.s_t = tf.placeholder(dtype=tf.float32,
shape=[
None,
self.replay_memory.history_length,
self.replay_memory.num_channels
])
self.q = DeepSense(params,
self.logger,
self.sess,
self.config,
name=Q_NETWORK)
self.q.build_model(self.s_t)
with tf.variable_scope(TARGET):
self.t_s_t = tf.placeholder(dtype=tf.float32,
shape=[
None,
self.replay_memory.history_length,
self.replay_memory.num_channels
])
self.t_q = DeepSense(params,
self.logger,
self.sess,
self.config,
name=T_Q_NETWORK)
self.t_q.build_model(self.t_s_t, train=False)
with tf.variable_scope(UPDATE_TARGET_NETWORK):
self.q_weights_placeholders = {}
self.t_weights_assign_ops = {}
for name in self.q.weights.keys():
self.q_weights_placeholders[name] = tf.placeholder(
tf.float32, self.q.weights[name].get_shape().as_list())
for name in self.q.weights.keys():
self.t_weights_assign_ops[name] = self.t_q.weights[
name].assign(self.q_weights_placeholders[name])
with tf.variable_scope(TRAINING):
self.target_q = tf.placeholder(tf.float32, [None], name=TARGET_Q)
self.action = tf.placeholder(tf.int64, [None], name=ACTION)
action_one_hot = tf.one_hot(self.action,
self.env.action_size,
1.0,
0.0,
name=ACTION_ONE_HOT)
q_acted = tf.reduce_sum(self.q.values * action_one_hot,
reduction_indices=1,
name=Q_ACTED)
with tf.variable_scope(LOSS):
self.delta = self.target_q - q_acted
self.global_step = tf.Variable(0, trainable=False)
self.loss = tf.reduce_mean(clipped_error(self.delta),
name=LOSS)
with tf.variable_scope(OPTIMIZER):
self.learning_rate_step = tf.placeholder(
tf.int64, None, name=LEARNING_RATE_STEP)
self.learning_rate_op = tf.maximum(
self.learning_rate_minimum,
tf.train.exponential_decay(self.learning_rate,
self.learning_rate_step,
self.learning_rate_decay_step,
self.learning_rate_decay,
staircase=True))
self.optimizer = tf.train.RMSPropOptimizer(
self.learning_rate_op, momentum=0.95,
epsilon=0.01).minimize(self.loss)
with tf.variable_scope(SUMMARY):
scalar_summary_tags = ['average.reward', 'average.loss', 'average.q', \
'episode.max reward', 'episode.min reward', 'episode.avg reward', \
'episode.num of game', 'training.learning_rate']
self.summary_placeholders = {}
self.summary_ops = {}
for tag in scalar_summary_tags:
self.summary_placeholders[tag] = \
tf.placeholder('float32', None, name=tag.replace(' ', '_'))
self.summary_ops[tag] = \
tf.summary.scalar(
name="{}-{}".format(self.env_name, tag),
tensor=self.summary_placeholders[tag]
)
histogram_summary_tags = ['episode.rewards', 'episode.actions']
for tag in histogram_summary_tags:
self.summary_placeholders[tag] = \
tf.placeholder('float32', None, name=tag.replace(' ', '_'))
self.summary_ops[tag] = \
tf.summary.histogram(
name=tag,
self.summary_placeholders[tag]
)
self._summary_writer = tf.summary.FileWriter(
config[TENSORBOARD_LOG_DIR])
self._summary_writer.add_graph(sess.graph)
tf.initialize_all_variables().run()
self._saver = tf.train.Saver(self.q.weights.values + [self.step_op],
max_to_keep=30)
self.load_model()
self.update_target_network()