Delete custom_models.py

RLLib_training/custom_models.py

-from ray.rllib.models import ModelCatalog, Model
-from ray.rllib.models.misc import normc_initializer
-
-import tensorflow as tf
-
-
-class ConvModelGlobalObs(Model):
-    def _build_layers_v2(self, input_dict, num_outputs, options):
-        """Define the layers of a custom model.
-        Arguments:
-            input_dict (dict): Dictionary of input tensors, including "obs",
-                "prev_action", "prev_reward", "is_training".
-            num_outputs (int): Output tensor must be of size
-                [BATCH_SIZE, num_outputs].
-            options (dict): Model options.
-        Returns:
-            (outputs, feature_layer): Tensors of size [BATCH_SIZE, num_outputs]
-                and [BATCH_SIZE, desired_feature_size].
-        When using dict or tuple observation spaces, you can access
-        the nested sub-observation batches here as well:
-        Examples:
-            >>> print(input_dict)
-            {'prev_actions': <tf.Tensor shape=(?,) dtype=int64>,
-             'prev_rewards': <tf.Tensor shape=(?,) dtype=float32>,
-             'is_training': <tf.Tensor shape=(), dtype=bool>,
-             'obs': (observation, features)
-        """
-        # Convolutional Layer #1
-
-        Relu = tf.nn.relu
-        BatchNormalization = tf.layers.batch_normalization
-        Dropout = tf.layers.dropout
-        Dense = tf.contrib.layers.fully_connected
-
-        map_size = int(input_dict['obs'][0].shape[0])
-
-        N_CHANNELS = 96
-
-        conv1 = Relu(self.conv2d(input_dict['obs'], N_CHANNELS, 'valid', strides=(2, 2)))
-
-        # conv2 = Relu(self.conv2d(conv1, 64, 'valid'))
-
-        # conv3 = Relu(self.conv2d(conv2, 64, 'valid'))
-
-        conv2_flat = tf.reshape(conv1, [-1, int(N_CHANNELS * ((map_size-3 + 1)/2)**2)])
-        # conv4_feature = tf.concat((conv2_flat, input_dict['obs'][1]), axis=1)
-        s_fc1 = Relu(Dense(conv2_flat, 256))
-        layerN_minus_1 = Relu(Dense(s_fc1, 64))
-        layerN = Dense(layerN_minus_1, num_outputs)
-        return layerN, layerN_minus_1
-
-    def conv2d(self, x, out_channels, padding, strides=(1,1)):
-        return tf.layers.conv2d(x, out_channels, kernel_size=[3, 3], padding=padding,
-                                use_bias=True, strides=strides)
-
-
-class LightModel(Model):
-    def _build_layers_v2(self, input_dict, num_outputs, options):
-        """Define the layers of a custom model.
-        Arguments:
-            input_dict (dict): Dictionary of input tensors, including "obs",
-                "prev_action", "prev_reward", "is_training".
-            num_outputs (int): Output tensor must be of size
-                [BATCH_SIZE, num_outputs].
-            options (dict): Model options.
-        Returns:
-            (outputs, feature_layer): Tensors of size [BATCH_SIZE, num_outputs]
-                and [BATCH_SIZE, desired_feature_size].
-        When using dict or tuple observation spaces, you can access
-        the nested sub-observation batches here as well:
-        Examples:
-            >>> print(input_dict)
-            {'prev_actions': <tf.Tensor shape=(?,) dtype=int64>,
-             'prev_rewards': <tf.Tensor shape=(?,) dtype=float32>,
-             'is_training': <tf.Tensor shape=(), dtype=bool>,
-             'obs': (observation, features)
-        """
-        # print(input_dict)
-        # Convolutional Layer #1
-        self.sess = tf.get_default_session()
-        Relu = tf.nn.relu
-        BatchNormalization = tf.layers.batch_normalization
-        Dropout = tf.layers.dropout
-        Dense = tf.contrib.layers.fully_connected
-
-        #conv1 = Relu(self.conv2d(input_dict['obs'][0], 32, 'valid'))
-        conv1 = Relu(self.conv2d(input_dict['obs'], 32, 'valid'))
-        conv2 = Relu(self.conv2d(conv1, 16, 'valid'))
-
-        # conv3 = Relu(self.conv2d(conv2, 64, 'valid'))
-
-        conv4_flat = tf.reshape(conv2, [-1, 16 * (17-2*2)**2])
-        #conv4_feature = tf.concat((conv4_flat, input_dict['obs'][1]), axis=1)
-        s_fc1 = Relu(Dense(conv4_flat, 128, weights_initializer=normc_initializer(1.0)))
-        # layerN_minus_1 = Relu(Dense(s_fc1, 256, use_bias=False))
-        layerN = Dense(s_fc1, num_outputs, weights_initializer=normc_initializer(0.01))
-        return layerN, s_fc1
-
-    def conv2d(self, x, out_channels, padding):
-        return tf.layers.conv2d(x, out_channels, kernel_size=[3, 3], padding=padding, use_bias=True)
-                                # weights_initializer=normc_initializer(1.0))