import logging import numpy as np import tensorflow as tf import tensorflow.contrib.layers as c_layers logger = logging.getLogger("unityagents") def create_agent_model(brain, lr=1e-4, h_size=128, epsilon=0.2, beta=1e-3, max_step=5e6, normalize=False, use_recurrent=False, num_layers=2, m_size=None): """ Takes a Unity environment and model-specific hyper-parameters and returns the appropriate PPO agent model for the environment. :param brain: BrainInfo used to generate specific network graph. :param lr: Learning rate. :param h_size: Size of hidden layers/ :param epsilon: Value for policy-divergence threshold. :param beta: Strength of entropy regularization. :return: a sub-class of PPOAgent tailored to the environment. :param max_step: Total number of training steps. :param normalize: Whether to normalize vector observation input. :param use_recurrent: Whether to use an LSTM layer in the network. :param num_layers Number of hidden layers between encoded input and policy & value layers """ if num_layers < 1: num_layers = 1 if brain.action_space_type == "continuous": return ContinuousControlModel(lr, brain, h_size, epsilon, max_step, normalize, use_recurrent, num_layers, m_size) if brain.action_space_type == "discrete": return DiscreteControlModel(lr, brain, h_size, epsilon, beta, max_step, normalize, use_recurrent, num_layers, m_size) class PPOModel(object): def __init__(self, m_size, normalize, use_recurrent): self.normalize = False self.use_recurrent = False self.observation_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.m_size = m_size self.global_step, self.increment_step = self.create_global_steps() self.last_reward, self.new_reward, self.update_reward = self.create_reward_encoder() self.normalize = normalize self.use_recurrent = use_recurrent self.state_in = None @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 create_reward_encoder(): """Creates TF ops to track and increment recent average cumulative reward.""" last_reward = tf.Variable(0, name="last_reward", trainable=False, dtype=tf.float32) new_reward = tf.placeholder(shape=[], dtype=tf.float32, name='new_reward') update_reward = tf.assign(last_reward, new_reward) return last_reward, new_reward, update_reward def create_recurrent_encoder(self, input_state, memory_in, name='lstm'): """ Builds a recurrent encoder for either state or observations (LSTM). :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, self.sequence_length, s_size]) _half_point = int(m_size / 2) with tf.variable_scope(name): rnn_cell = tf.contrib.rnn.BasicLSTMCell(_half_point) lstm_state_in = tf.contrib.rnn.LSTMStateTuple(memory_in[:, :_half_point], memory_in[:, _half_point:]) recurrent_state, lstm_state_out = tf.nn.dynamic_rnn(rnn_cell, lstm_input_state, initial_state=lstm_state_in, time_major=False, dtype=tf.float32) recurrent_state = tf.reshape(recurrent_state, shape=[-1, _half_point]) return recurrent_state, tf.concat([lstm_state_out.c, lstm_state_out.h], axis=1) def create_visual_encoder(self, o_size_h, o_size_w, bw, h_size, num_streams, activation, num_layers): """ Builds a set of visual (CNN) encoders. :param o_size_h: Height observation size. :param o_size_w: Width observation size. :param bw: Whether image is greyscale {True} or color {False}. :param h_size: Hidden layer size. :param num_streams: Number of visual streams to construct. :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. """ if bw: c_channels = 1 else: c_channels = 3 self.observation_in.append(tf.placeholder(shape=[None, o_size_h, o_size_w, c_channels], dtype=tf.float32, name='observation_%d' % len(self.observation_in))) streams = [] for i in range(num_streams): conv1 = tf.layers.conv2d(self.observation_in[-1], 16, kernel_size=[8, 8], strides=[4, 4], activation=tf.nn.elu) conv2 = tf.layers.conv2d(conv1, 32, kernel_size=[4, 4], strides=[2, 2], activation=tf.nn.elu) hidden = c_layers.flatten(conv2) for j in range(num_layers): hidden = tf.layers.dense(hidden, h_size, use_bias=False, activation=activation) streams.append(hidden) return streams def create_continuous_state_encoder(self, s_size, h_size, num_streams, activation, num_layers): """ Builds a set of hidden state encoders. :param s_size: state input size. :param h_size: Hidden layer size. :param num_streams: Number of state streams to construct. :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. """ self.state_in = tf.placeholder(shape=[None, s_size], dtype=tf.float32, name='state') if self.normalize: self.running_mean = tf.get_variable("running_mean", [s_size], trainable=False, dtype=tf.float32, initializer=tf.zeros_initializer()) self.running_variance = tf.get_variable("running_variance", [s_size], trainable=False, dtype=tf.float32, initializer=tf.ones_initializer()) self.normalized_state = tf.clip_by_value((self.state_in - self.running_mean) / tf.sqrt( self.running_variance / (tf.cast(self.global_step, tf.float32) + 1)), -5, 5, name="normalized_state") self.new_mean = tf.placeholder(shape=[s_size], dtype=tf.float32, name='new_mean') self.new_variance = tf.placeholder(shape=[s_size], dtype=tf.float32, name='new_variance') self.update_mean = tf.assign(self.running_mean, self.new_mean) self.update_variance = tf.assign(self.running_variance, self.new_variance) else: self.normalized_state = self.state_in streams = [] for i in range(num_streams): hidden = self.normalized_state for j in range(num_layers): hidden = tf.layers.dense(hidden, h_size, activation=activation, kernel_initializer=c_layers.variance_scaling_initializer(1.0)) streams.append(hidden) return streams def create_discrete_state_encoder(self, s_size, h_size, num_streams, activation, num_layers): """ Builds a set of hidden state encoders from discrete state input. :param s_size: state input size (discrete). :param h_size: Hidden layer size. :param num_streams: Number of state streams to construct. :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. """ self.state_in = tf.placeholder(shape=[None, 1], dtype=tf.int32, name='state') state_in = tf.reshape(self.state_in, [-1]) state_onehot = c_layers.one_hot_encoding(state_in, s_size) streams = [] hidden = state_onehot for i in range(num_streams): for j in range(num_layers): hidden = tf.layers.dense(hidden, h_size, use_bias=False, activation=activation) streams.append(hidden) return streams def create_ppo_optimizer(self, probs, old_probs, value, entropy, beta, epsilon, lr, max_step): """ Creates training-specific Tensorflow ops for PPO models. :param probs: Current policy probabilities :param old_probs: Past policy probabilities :param value: Current value estimate :param beta: Entropy regularization strength :param entropy: Current policy entropy :param epsilon: Value for policy-divergence threshold :param lr: Learning rate :param max_step: Total number of training steps. """ self.returns_holder = tf.placeholder(shape=[None], dtype=tf.float32, name='discounted_rewards') self.advantage = tf.placeholder(shape=[None, 1], dtype=tf.float32, name='advantages') decay_epsilon = tf.train.polynomial_decay(epsilon, self.global_step, max_step, 0.1, power=1.0) r_theta = probs / (old_probs + 1e-10) p_opt_a = r_theta * self.advantage p_opt_b = tf.clip_by_value(r_theta, 1 - decay_epsilon, 1 + decay_epsilon) * self.advantage self.policy_loss = -tf.reduce_mean(tf.minimum(p_opt_a, p_opt_b)) self.value_loss = tf.reduce_mean(tf.squared_difference(self.returns_holder, tf.reduce_sum(value, axis=1))) decay_beta = tf.train.polynomial_decay(beta, self.global_step, max_step, 1e-5, power=1.0) self.loss = self.policy_loss + 0.5 * self.value_loss - decay_beta * tf.reduce_mean(entropy) self.learning_rate = tf.train.polynomial_decay(lr, self.global_step, max_step, 1e-10, power=1.0) optimizer = tf.train.AdamOptimizer(learning_rate=self.learning_rate) self.update_batch = optimizer.minimize(self.loss) class ContinuousControlModel(PPOModel): def __init__(self, lr, brain, h_size, epsilon, max_step, normalize, use_recurrent, num_layers, m_size): """ Creates Continuous Control Actor-Critic model. :param brain: State-space size :param h_size: Hidden layer size """ super(ContinuousControlModel, self).__init__(m_size, normalize, use_recurrent) a_size = brain.action_space_size hidden_state, hidden_visual, hidden_policy, hidden_value = None, None, None, None if brain.number_observations > 0: visual_encoder_0 = [] visual_encoder_1 = [] for i in range(brain.number_observations): height_size, width_size = brain.camera_resolutions[i]['height'], brain.camera_resolutions[i]['width'] bw = brain.camera_resolutions[i]['blackAndWhite'] encoded_visual = self.create_visual_encoder(height_size, width_size, bw, h_size, 2, tf.nn.tanh, num_layers) visual_encoder_0.append(encoded_visual[0]) visual_encoder_1.append(encoded_visual[1]) hidden_visual = [tf.concat(visual_encoder_0, axis=1), tf.concat(visual_encoder_1, axis=1)] if brain.state_space_size > 0: s_size = brain.state_space_size * brain.stacked_states if brain.state_space_type == "continuous": hidden_state = self.create_continuous_state_encoder(s_size, h_size, 2, tf.nn.tanh, num_layers) else: hidden_state = self.create_discrete_state_encoder(s_size, h_size, 2, tf.nn.tanh, num_layers) if hidden_visual is None and hidden_state is None: raise Exception("No valid network configuration possible. " "There are no states or observations in this brain") elif hidden_visual is not None and hidden_state is None: hidden_policy, hidden_value = hidden_visual elif hidden_visual is None and hidden_state is not None: hidden_policy, hidden_value = hidden_state elif hidden_visual is not None and hidden_state is not None: hidden_policy = tf.concat([hidden_visual[0], hidden_state[0]], axis=1) hidden_value = tf.concat([hidden_visual[1], hidden_state[1]], axis=1) 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_policy, self.memory_in[:, :_half_point], name='lstm_policy') hidden_value, memory_value_out = self.create_recurrent_encoder( hidden_value, self.memory_in[:, _half_point:], name='lstm_value') self.memory_out = tf.concat([memory_policy_out, memory_value_out], axis=1, name='recurrent_out') self.mu = tf.layers.dense(hidden_policy, a_size, activation=None, use_bias=False, kernel_initializer=c_layers.variance_scaling_initializer(factor=0.01)) self.log_sigma_sq = tf.get_variable("log_sigma_squared", [a_size], dtype=tf.float32, initializer=tf.zeros_initializer()) self.sigma_sq = tf.exp(self.log_sigma_sq) self.epsilon = tf.random_normal(tf.shape(self.mu), dtype=tf.float32) self.output = self.mu + tf.sqrt(self.sigma_sq) * self.epsilon self.output = tf.identity(self.output, name='action') a = tf.exp(-1 * tf.pow(tf.stop_gradient(self.output) - self.mu, 2) / (2 * self.sigma_sq)) b = 1 / tf.sqrt(2 * self.sigma_sq * np.pi) self.probs = tf.multiply(a, b, name="action_probs") self.entropy = tf.reduce_sum(0.5 * tf.log(2 * np.pi * np.e * self.sigma_sq)) self.value = tf.layers.dense(hidden_value, 1, activation=None) self.value = tf.identity(self.value, name="value_estimate") self.old_probs = tf.placeholder(shape=[None, a_size], dtype=tf.float32, name='old_probabilities') self.create_ppo_optimizer(self.probs, self.old_probs, self.value, self.entropy, 0.0, epsilon, lr, max_step) class DiscreteControlModel(PPOModel): def __init__(self, lr, brain, h_size, epsilon, beta, max_step, normalize, use_recurrent, num_layers, m_size): """ Creates Discrete Control Actor-Critic model. :param brain: State-space size :param h_size: Hidden layer size """ super(DiscreteControlModel, self).__init__(m_size, normalize, use_recurrent) a_size = brain.action_space_size hidden_state, hidden_visual, hidden = None, None, None if brain.number_observations > 0: visual_encoders = [] for i in range(brain.number_observations): height_size, width_size = brain.camera_resolutions[i]['height'], brain.camera_resolutions[i]['width'] bw = brain.camera_resolutions[i]['blackAndWhite'] visual_encoders.append( self.create_visual_encoder(height_size, width_size, bw, h_size, 1, tf.nn.elu, num_layers)[0]) hidden_visual = tf.concat(visual_encoders, axis=1) if brain.state_space_size > 0: s_size = brain.state_space_size * brain.stacked_states if brain.state_space_type == "continuous": hidden_state = \ self.create_continuous_state_encoder(s_size, h_size, 1, tf.nn.elu, num_layers)[0] else: hidden_state = self.create_discrete_state_encoder(s_size, h_size, 1, tf.nn.elu, num_layers)[0] if hidden_visual is None and hidden_state is None: raise Exception("No valid network configuration possible. " "There are no states or observations in this brain") elif hidden_visual is not None and hidden_state is None: hidden = hidden_visual elif hidden_visual is None and hidden_state is not None: hidden = hidden_state elif hidden_visual is not None and hidden_state is not None: hidden = tf.concat([hidden_visual, hidden_state], axis=1) if self.use_recurrent: self.memory_in = tf.placeholder(shape=[None, self.m_size], dtype=tf.float32, name='recurrent_in') hidden, self.memory_out = self.create_recurrent_encoder(hidden, self.memory_in) self.memory_out = tf.identity(self.memory_out, name='recurrent_out') self.policy = tf.layers.dense(hidden, a_size, activation=None, use_bias=False, kernel_initializer=c_layers.variance_scaling_initializer(factor=0.01)) self.probs = tf.nn.softmax(self.policy, name="action_probs") self.output = tf.multinomial(self.policy, 1) self.output = tf.identity(self.output, name="action") self.value = tf.layers.dense(hidden, 1, activation=None) self.value = tf.identity(self.value, name="value_estimate") self.entropy = -tf.reduce_sum(self.probs * tf.log(self.probs + 1e-10), axis=1) self.action_holder = tf.placeholder(shape=[None], dtype=tf.int32) self.selected_actions = c_layers.one_hot_encoding(self.action_holder, a_size) self.old_probs = tf.placeholder(shape=[None, a_size], dtype=tf.float32, name='old_probabilities') self.responsible_probs = tf.reduce_sum(self.probs * self.selected_actions, axis=1) self.old_responsible_probs = tf.reduce_sum(self.old_probs * self.selected_actions, axis=1) self.create_ppo_optimizer(self.responsible_probs, self.old_responsible_probs, self.value, self.entropy, beta, epsilon, lr, max_step)