import logging import numpy as np import tensorflow as tf import tensorflow.contrib.layers as c_layers logger = logging.getLogger("mlagents.envs") class LearningModel(object): _version_number_ = 1 def __init__(self, m_size, normalize, use_recurrent, brain, seed): tf.set_random_seed(seed) self.brain = brain self.vector_in = None self.global_step, self.increment_step = self.create_global_steps() self.visual_in = [] self.batch_size = tf.placeholder(shape=None, dtype=tf.int32, name='batch_size') self.sequence_length = tf.placeholder(shape=None, dtype=tf.int32, name='sequence_length') self.mask_input = tf.placeholder(shape=[None], dtype=tf.float32, name='masks') self.mask = tf.cast(self.mask_input, tf.int32) self.use_recurrent = use_recurrent if self.use_recurrent: self.m_size = m_size else: self.m_size = 0 self.normalize = normalize self.act_size = brain.vector_action_space_size self.vec_obs_size = brain.vector_observation_space_size * \ brain.num_stacked_vector_observations self.vis_obs_size = brain.number_visual_observations tf.Variable(int(brain.vector_action_space_type == 'continuous'), name='is_continuous_control', trainable=False, dtype=tf.int32) tf.Variable(self._version_number_, name='version_number', trainable=False, dtype=tf.int32) tf.Variable(self.m_size, name="memory_size", trainable=False, dtype=tf.int32) if brain.vector_action_space_type == 'continuous': tf.Variable(self.act_size[0], name="action_output_shape", trainable=False, dtype=tf.int32) else: tf.Variable(sum(self.act_size), name="action_output_shape", trainable=False, dtype=tf.int32) @staticmethod def create_global_steps(): """Creates TF ops to track and increment global training step.""" global_step = tf.Variable(0, name="global_step", trainable=False, dtype=tf.int32) increment_step = tf.assign(global_step, tf.add(global_step, 1)) return global_step, increment_step @staticmethod def swish(input_activation): """Swish activation function. For more info: https://arxiv.org/abs/1710.05941""" return tf.multiply(input_activation, tf.nn.sigmoid(input_activation)) @staticmethod def create_visual_input(camera_parameters, name): """ Creates image input op. :param camera_parameters: Parameters for visual observation from BrainInfo. :param name: Desired name of input op. :return: input op. """ o_size_h = camera_parameters['height'] o_size_w = camera_parameters['width'] bw = camera_parameters['blackAndWhite'] if bw: c_channels = 1 else: c_channels = 3 visual_in = tf.placeholder(shape=[None, o_size_h, o_size_w, c_channels], dtype=tf.float32, name=name) return visual_in def create_vector_input(self, name='vector_observation'): """ Creates ops for vector observation input. :param name: Name of the placeholder op. :param vec_obs_size: Size of stacked vector observation. :return: """ self.vector_in = tf.placeholder(shape=[None, self.vec_obs_size], dtype=tf.float32, name=name) if self.normalize: self.running_mean = tf.get_variable("running_mean", [self.vec_obs_size], trainable=False, dtype=tf.float32, initializer=tf.zeros_initializer()) self.running_variance = tf.get_variable("running_variance", [self.vec_obs_size], trainable=False, dtype=tf.float32, initializer=tf.ones_initializer()) self.update_mean, self.update_variance = self.create_normalizer_update(self.vector_in) self.normalized_state = tf.clip_by_value((self.vector_in - self.running_mean) / tf.sqrt( self.running_variance / (tf.cast(self.global_step, tf.float32) + 1)), -5, 5, name="normalized_state") return self.normalized_state else: return self.vector_in def create_normalizer_update(self, vector_input): mean_current_observation = tf.reduce_mean(vector_input, axis=0) new_mean = self.running_mean + (mean_current_observation - self.running_mean) / \ tf.cast(tf.add(self.global_step, 1), tf.float32) new_variance = self.running_variance + (mean_current_observation - new_mean) * \ (mean_current_observation - self.running_mean) update_mean = tf.assign(self.running_mean, new_mean) update_variance = tf.assign(self.running_variance, new_variance) return update_mean, update_variance @staticmethod def create_vector_observation_encoder(observation_input, h_size, activation, num_layers, scope, reuse): """ Builds a set of hidden state encoders. :param reuse: Whether to re-use the weights within the same scope. :param scope: Graph scope for the encoder ops. :param observation_input: Input vector. :param h_size: Hidden layer size. :param activation: What type of activation function to use for layers. :param num_layers: number of hidden layers to create. :return: List of hidden layer tensors. """ with tf.variable_scope(scope): hidden = observation_input for i in range(num_layers): hidden = tf.layers.dense(hidden, h_size, activation=activation, reuse=reuse, name="hidden_{}".format(i), kernel_initializer=c_layers.variance_scaling_initializer( 1.0)) return hidden def create_visual_observation_encoder(self, image_input, h_size, activation, num_layers, scope, reuse): """ Builds a set of visual (CNN) encoders. :param reuse: Whether to re-use the weights within the same scope. :param scope: The scope of the graph within which to create the ops. :param image_input: The placeholder for the image input to use. :param h_size: Hidden layer size. :param activation: What type of activation function to use for layers. :param num_layers: number of hidden layers to create. :return: List of hidden layer tensors. """ with tf.variable_scope(scope): conv1 = tf.layers.conv2d(image_input, 16, kernel_size=[8, 8], strides=[4, 4], activation=tf.nn.elu, reuse=reuse, name="conv_1") conv2 = tf.layers.conv2d(conv1, 32, kernel_size=[4, 4], strides=[2, 2], activation=tf.nn.elu, reuse=reuse, name="conv_2") hidden = c_layers.flatten(conv2) with tf.variable_scope(scope + '/' + 'flat_encoding'): hidden_flat = self.create_vector_observation_encoder(hidden, h_size, activation, num_layers, scope, reuse) return hidden_flat @staticmethod def create_discrete_action_masking_layer(all_logits, action_masks, action_size): """ Creates a masking layer for the discrete actions :param all_logits: The concatenated unnormalized action probabilities for all branches :param action_masks: The mask for the logits. Must be of dimension [None x total_number_of_action] :param action_size: A list containing the number of possible actions for each branch :return: The action output dimension [batch_size, num_branches] and the concatenated normalized logits """ action_idx = [0] + list(np.cumsum(action_size)) branches_logits = [all_logits[:, action_idx[i]:action_idx[i + 1]] for i in range(len(action_size))] branch_masks = [action_masks[:, action_idx[i]:action_idx[i + 1]] for i in range(len(action_size))] raw_probs = [tf.multiply(tf.nn.softmax(branches_logits[k]) + 1.0e-10, branch_masks[k]) for k in range(len(action_size))] normalized_probs = [ tf.divide(raw_probs[k], tf.reduce_sum(raw_probs[k], axis=1, keepdims=True)) for k in range(len(action_size))] output = tf.concat([tf.multinomial(tf.log(normalized_probs[k]), 1) for k in range(len(action_size))], axis=1) return output, tf.concat([tf.log(normalized_probs[k] + 1.0e-10) for k in range(len(action_size))], axis=1) def create_observation_streams(self, num_streams, h_size, num_layers): """ Creates encoding stream for observations. :param num_streams: Number of streams to create. :param h_size: Size of hidden linear layers in stream. :param num_layers: Number of hidden linear layers in stream. :return: List of encoded streams. """ brain = self.brain activation_fn = self.swish self.visual_in = [] for i in range(brain.number_visual_observations): visual_input = self.create_visual_input(brain.camera_resolutions[i], name="visual_observation_" + str(i)) self.visual_in.append(visual_input) vector_observation_input = self.create_vector_input() final_hiddens = [] for i in range(num_streams): visual_encoders = [] hidden_state, hidden_visual = None, None if self.vis_obs_size > 0: for j in range(brain.number_visual_observations): encoded_visual = self.create_visual_observation_encoder(self.visual_in[j], h_size, activation_fn, num_layers, "main_graph_{}_encoder{}" .format(i, j), False) visual_encoders.append(encoded_visual) hidden_visual = tf.concat(visual_encoders, axis=1) if brain.vector_observation_space_size > 0: hidden_state = self.create_vector_observation_encoder(vector_observation_input, h_size, activation_fn, num_layers, "main_graph_{}".format(i), False) if hidden_state is not None and hidden_visual is not None: final_hidden = tf.concat([hidden_visual, hidden_state], axis=1) elif hidden_state is None and hidden_visual is not None: final_hidden = hidden_visual elif hidden_state is not None and hidden_visual is None: final_hidden = hidden_state else: raise Exception("No valid network configuration possible. " "There are no states or observations in this brain") final_hiddens.append(final_hidden) return final_hiddens @staticmethod def create_recurrent_encoder(input_state, memory_in, sequence_length, name='lstm'): """ Builds a recurrent encoder for either state or observations (LSTM). :param sequence_length: Length of sequence to unroll. :param input_state: The input tensor to the LSTM cell. :param memory_in: The input memory to the LSTM cell. :param name: The scope of the LSTM cell. """ s_size = input_state.get_shape().as_list()[1] m_size = memory_in.get_shape().as_list()[1] lstm_input_state = tf.reshape(input_state, shape=[-1, sequence_length, s_size]) memory_in = tf.reshape(memory_in[:, :], [-1, m_size]) _half_point = int(m_size / 2) with tf.variable_scope(name): rnn_cell = tf.contrib.rnn.BasicLSTMCell(_half_point) lstm_vector_in = tf.contrib.rnn.LSTMStateTuple(memory_in[:, :_half_point], memory_in[:, _half_point:]) recurrent_output, lstm_state_out = tf.nn.dynamic_rnn(rnn_cell, lstm_input_state, initial_state=lstm_vector_in) recurrent_output = tf.reshape(recurrent_output, shape=[-1, _half_point]) return recurrent_output, tf.concat([lstm_state_out.c, lstm_state_out.h], axis=1) def create_cc_actor_critic(self, h_size, num_layers): """ Creates Continuous control actor-critic model. :param h_size: Size of hidden linear layers. :param num_layers: Number of hidden linear layers. """ hidden_streams = self.create_observation_streams(2, h_size, num_layers) if self.use_recurrent: self.memory_in = tf.placeholder(shape=[None, self.m_size], dtype=tf.float32, name='recurrent_in') _half_point = int(self.m_size / 2) hidden_policy, memory_policy_out = self.create_recurrent_encoder( hidden_streams[0], self.memory_in[:, :_half_point], self.sequence_length, name='lstm_policy') hidden_value, memory_value_out = self.create_recurrent_encoder( hidden_streams[1], self.memory_in[:, _half_point:], self.sequence_length, name='lstm_value') self.memory_out = tf.concat([memory_policy_out, memory_value_out], axis=1, name='recurrent_out') else: hidden_policy = hidden_streams[0] hidden_value = hidden_streams[1] mu = tf.layers.dense(hidden_policy, self.act_size[0], activation=None, kernel_initializer=c_layers.variance_scaling_initializer(factor=0.01)) log_sigma_sq = tf.get_variable("log_sigma_squared", [self.act_size[0]], dtype=tf.float32, initializer=tf.zeros_initializer()) sigma_sq = tf.exp(log_sigma_sq) self.epsilon = tf.placeholder(shape=[None, self.act_size[0]], dtype=tf.float32, name='epsilon') # Clip and scale output to ensure actions are always within [-1, 1] range. self.output_pre = mu + tf.sqrt(sigma_sq) * self.epsilon output_post = tf.clip_by_value(self.output_pre, -3, 3) / 3 self.output = tf.identity(output_post, name='action') self.selected_actions = tf.stop_gradient(output_post) # Compute probability of model output. all_probs = - 0.5 * tf.square(tf.stop_gradient(self.output_pre) - mu) / sigma_sq \ - 0.5 * tf.log(2.0 * np.pi) - 0.5 * log_sigma_sq self.all_log_probs = tf.identity(all_probs, name='action_probs') self.entropy = 0.5 * tf.reduce_mean(tf.log(2 * np.pi * np.e) + log_sigma_sq) value = tf.layers.dense(hidden_value, 1, activation=None) self.value = tf.identity(value, name="value_estimate") self.all_old_log_probs = tf.placeholder(shape=[None, self.act_size[0]], dtype=tf.float32, name='old_probabilities') # We keep these tensors the same name, but use new nodes to keep code parallelism with discrete control. self.log_probs = tf.reduce_sum((tf.identity(self.all_log_probs)), axis=1, keepdims=True) self.old_log_probs = tf.reduce_sum((tf.identity(self.all_old_log_probs)), axis=1, keepdims=True) def create_dc_actor_critic(self, h_size, num_layers): """ Creates Discrete control actor-critic model. :param h_size: Size of hidden linear layers. :param num_layers: Number of hidden linear layers. """ hidden_streams = self.create_observation_streams(1, h_size, num_layers) hidden = hidden_streams[0] if self.use_recurrent: self.prev_action = tf.placeholder(shape=[None, len(self.act_size)], dtype=tf.int32, name='prev_action') prev_action_oh = tf.concat([ tf.one_hot(self.prev_action[:, i], self.act_size[i]) for i in range(len(self.act_size))], axis=1) hidden = tf.concat([hidden, prev_action_oh], axis=1) self.memory_in = tf.placeholder(shape=[None, self.m_size], dtype=tf.float32, name='recurrent_in') hidden, memory_out = self.create_recurrent_encoder(hidden, self.memory_in, self.sequence_length) self.memory_out = tf.identity(memory_out, name='recurrent_out') policy_branches = [] for size in self.act_size: policy_branches.append(tf.layers.dense(hidden, size, activation=None, use_bias=False, kernel_initializer=c_layers.variance_scaling_initializer(factor=0.01))) self.all_log_probs = tf.concat([branch for branch in policy_branches], axis=1, name="action_probs") self.action_masks = tf.placeholder(shape=[None, sum(self.act_size)], dtype=tf.float32, name="action_masks") output, normalized_logits = self.create_discrete_action_masking_layer( self.all_log_probs, self.action_masks, self.act_size) self.output = tf.identity(output) self.normalized_logits = tf.identity(normalized_logits, name='action') value = tf.layers.dense(hidden, 1, activation=None) self.value = tf.identity(value, name="value_estimate") self.action_holder = tf.placeholder( shape=[None, len(policy_branches)], dtype=tf.int32, name="action_holder") self.action_oh = tf.concat([ tf.one_hot(self.action_holder[:, i], self.act_size[i]) for i in range(len(self.act_size))], axis=1) self.selected_actions = tf.stop_gradient(self.action_oh) self.all_old_log_probs = tf.placeholder( shape=[None, sum(self.act_size)], dtype=tf.float32, name='old_probabilities') _, old_normalized_logits = self.create_discrete_action_masking_layer( self.all_old_log_probs, self.action_masks, self.act_size) action_idx = [0] + list(np.cumsum(self.act_size)) self.entropy = tf.reduce_sum((tf.stack([ tf.nn.softmax_cross_entropy_with_logits_v2( labels=tf.nn.softmax(self.all_log_probs[:, action_idx[i]:action_idx[i + 1]]), logits=self.all_log_probs[:, action_idx[i]:action_idx[i + 1]]) for i in range(len(self.act_size))], axis=1)), axis=1) self.log_probs = tf.reduce_sum((tf.stack([ -tf.nn.softmax_cross_entropy_with_logits_v2( labels=self.action_oh[:, action_idx[i]:action_idx[i + 1]], logits=normalized_logits[:, action_idx[i]:action_idx[i + 1]] ) for i in range(len(self.act_size))], axis=1)), axis=1, keepdims=True) self.old_log_probs = tf.reduce_sum((tf.stack([ -tf.nn.softmax_cross_entropy_with_logits_v2( labels=self.action_oh[:, action_idx[i]:action_idx[i + 1]], logits=old_normalized_logits[:, action_idx[i]:action_idx[i + 1]] ) for i in range(len(self.act_size))], axis=1)), axis=1, keepdims=True)