Tanh = tf.nn.tanh BatchNormalization = tf.layers.batch_normalization Dense = tf.layers.dense Dropout = tf.nn.dropout x = tf.placeholder(tf.float64, [None, input_count], name= 'input_node') target = tf.placeholder(tf.float64, [None, output_count], name = "target_node") keep_prob = tf.placeholder(tf.float64, [], name = "keep_prob_node") learning_rate = tf.placeholder(tf.float64, [], name = "learning_rate_node") Q_target = tf.slice(target, [0, 0], [-1, Q_output_count], name = "Q_slice_node") action_target = tf.slice(target, [0, Q_output_count], [-1, action_output_count], name = "action_slice_node") hidden_1 = Dense(x, hidden_count_1, tf.nn.relu, use_bias = True, kernel_initializer = tf.glorot_normal_initializer(), name = "hidden_1") hidden_2 = Dense(Dropout(hidden_1, keep_prob=keep_prob), hidden_count_2, tf.nn.relu, use_bias = True, kernel_initializer = tf.glorot_normal_initializer(), name = "hidden_2") # hidden_3 = Dense(Dropout(hidden_2, keep_prob=keep_prob), hidden_count_3, tf.nn.relu, use_bias = True, kernel_initializer = tf.glorot_normal_initializer(), name = "hidden_3") # hidden_4 = Dense(Dropout(hidden_3, keep_prob=keep_prob), hidden_count_4, tf.nn.relu, use_bias = True, kernel_initializer = tf.glorot_normal_initializer(), name = "hidden_4") # hidden_5 = Dense(Dropout(hidden_4, keep_prob=keep_prob), hidden_count_5, tf.nn.relu, use_bias = True, kernel_initializer = tf.glorot_normal_initializer(), name = "hidden_5") Q_output = tf.layers.dense(hidden_2, Q_output_count, use_bias = True, kernel_initializer = tf.zeros_initializer, bias_initializer = tf.zeros_initializer, name = 'Q_output_node') action_output = tf.layers.dense(hidden_2, action_output_count, tf.nn.softmax, use_bias = True, kernel_initializer = tf.zeros_initializer, bias_initializer = tf.zeros_initializer, name = "action_output_node") prediction = tf.concat([Q_output, action_output], 1, name = "concat_node") prediction_identity = tf.identity(prediction, name = "prediction_node") Q_loss = tf.keras.losses.mean_squared_error(y_true = Q_target, y_pred = Q_output) policy_loss = tf.keras.losses.categorical_crossentropy(y_true = action_target, y_pred = action_output) total_loss = Q_loss + policy_loss
Tanh = tf.nn.tanh BatchNormalization = tf.layers.batch_normalization Dense = tf.layers.dense Dropout = tf.nn.dropout x = tf.placeholder(tf.float64, [None, input_count], name= 'input_node') target = tf.placeholder(tf.float64, [None, output_count], name = "target_node") keep_prob = tf.placeholder(tf.float64, [], name = "keep_prob_node") learning_rate = tf.placeholder(tf.float64, [], name = "learning_rate_node") Q_target = tf.slice(target, [0, 0], [-1, Q_output_count], name = "Q_slice_node") action_target = tf.slice(target, [0, Q_output_count], [-1, action_output_count], name = "action_slice_node") hidden_1 = Dense(x, hidden_count_1, tf.nn.relu, use_bias = True, kernel_initializer = tf.glorot_normal_initializer(), name = "hidden_1") Q_output_raw = tf.layers.dense(hidden_1, Q_output_count, use_bias = True, kernel_initializer = tf.zeros_initializer, bias_initializer = tf.zeros_initializer, name = 'Q_output_node') Q_output_ = tf.multiply(Q_output_raw, 100000) Q_output_ = tf.round(Q_output_) Q_output = tf.div(Q_output_, 100000) action_output_raw = tf.layers.dense(hidden_1, action_output_count, tf.nn.softmax, use_bias = True, kernel_initializer = tf.zeros_initializer, bias_initializer = tf.zeros_initializer, name = "action_output_node") action_output_ = tf.multiply(action_output_raw, 100000) action_output_ = tf.round(action_output_) action_output = tf.div(action_output_, 100000) prediction = tf.concat([Q_output, action_output], 1, name = "concat_node") prediction_identity = tf.identity(prediction, name = "prediction_node") Q_loss = tf.keras.losses.mean_squared_error(y_true = Q_target, y_pred = Q_output_raw)
Tanh = tf.nn.tanh
BatchNormalization = tf.layers.batch_normalization
Dense = tf.layers.dense
Dropout = tf.nn.dropout
x = tf.placeholder(tf.float64, [None, input_count], name='input_node')
Q_target = tf.placeholder(tf.float64, [None, output_count], name="target_node")
keep_prob = tf.placeholder(tf.float64, [], name="keep_prob_node")
learning_rate = tf.placeholder(tf.float64, [], name="learning_rate_node")
hidden_1 = Dense(x,
hidden_count_1,
tf.nn.relu,
use_bias=True,
kernel_initializer=tf.glorot_normal_initializer(),
name="hidden_1")
hidden_2 = Dense(Dropout(hidden_1, keep_prob=keep_prob),
hidden_count_2,
tf.nn.relu,
use_bias=True,
kernel_initializer=tf.glorot_normal_initializer(),
name="hidden_2")
hidden_3 = Dense(Dropout(hidden_2, keep_prob=keep_prob),
hidden_count_3,
tf.nn.relu,
use_bias=True,
kernel_initializer=tf.glorot_normal_initializer(),
name="hidden_3")
hidden_4 = Dense(Dropout(hidden_3, keep_prob=keep_prob),
hidden_count_4,
