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320 行
16 KiB
320 行
16 KiB
import numpy as np
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import tensorflow as tf
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import tensorflow.contrib.layers as c_layers
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from tensorflow.python.tools import freeze_graph
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from unityagents import UnityEnvironmentException
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def create_agent_model(env, lr=1e-4, h_size=128, epsilon=0.2, beta=1e-3, max_step=5e6, normalize=False, num_layers=2):
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"""
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Takes a Unity environment and model-specific hyper-parameters and returns the
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appropriate PPO agent model for the environment.
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:param env: a Unity environment.
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:param lr: Learning rate.
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:param h_size: Size of hidden layers/
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:param epsilon: Value for policy-divergence threshold.
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:param beta: Strength of entropy regularization.
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:return: a sub-class of PPOAgent tailored to the environment.
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:param max_step: Total number of training steps.
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"""
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if num_layers < 1: num_layers = 1
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brain_name = env.brain_names[0]
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brain = env.brains[brain_name]
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if brain.action_space_type == "continuous":
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return ContinuousControlModel(lr, brain, h_size, epsilon, max_step, normalize, num_layers)
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if brain.action_space_type == "discrete":
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return DiscreteControlModel(lr, brain, h_size, epsilon, beta, max_step, normalize, num_layers)
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def save_model(sess, saver, model_path="./", steps=0):
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"""
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Saves current model to checkpoint folder.
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:param sess: Current Tensorflow session.
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:param model_path: Designated model path.
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:param steps: Current number of steps in training process.
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:param saver: Tensorflow saver for session.
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"""
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last_checkpoint = model_path + '/model-' + str(steps) + '.cptk'
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saver.save(sess, last_checkpoint)
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tf.train.write_graph(sess.graph_def, model_path, 'raw_graph_def.pb', as_text=False)
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print("Saved Model")
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def export_graph(model_path, env_name="env", target_nodes="action,value_estimate,action_probs"):
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"""
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Exports latest saved model to .bytes format for Unity embedding.
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:param model_path: path of model checkpoints.
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:param env_name: Name of associated Learning Environment.
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:param target_nodes: Comma separated string of needed output nodes for embedded graph.
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"""
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ckpt = tf.train.get_checkpoint_state(model_path)
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freeze_graph.freeze_graph(input_graph=model_path + '/raw_graph_def.pb',
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input_binary=True,
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input_checkpoint=ckpt.model_checkpoint_path,
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output_node_names=target_nodes,
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output_graph=model_path + '/' + env_name + '.bytes',
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clear_devices=True, initializer_nodes="", input_saver="",
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restore_op_name="save/restore_all", filename_tensor_name="save/Const:0")
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class PPOModel(object):
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def __init__(self):
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self.normalize = False
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def create_global_steps(self):
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"""Creates TF ops to track and increment global training step."""
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self.global_step = tf.Variable(0, name="global_step", trainable=False, dtype=tf.int32)
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self.increment_step = tf.assign(self.global_step, self.global_step + 1)
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def create_reward_encoder(self):
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"""Creates TF ops to track and increment recent average cumulative reward."""
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self.last_reward = tf.Variable(0, name="last_reward", trainable=False, dtype=tf.float32)
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self.new_reward = tf.placeholder(shape=[], dtype=tf.float32, name='new_reward')
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self.update_reward = tf.assign(self.last_reward, self.new_reward)
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def create_visual_encoder(self, o_size_h, o_size_w, bw, h_size, num_streams, activation, num_layers):
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"""
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Builds a set of visual (CNN) encoders.
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:param o_size_h: Height observation size.
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:param o_size_w: Width observation size.
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:param bw: Whether image is greyscale {True} or color {False}.
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:param h_size: Hidden layer size.
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:param num_streams: Number of visual streams to construct.
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:param activation: What type of activation function to use for layers.
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:return: List of hidden layer tensors.
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"""
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if bw:
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c_channels = 1
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else:
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c_channels = 3
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self.observation_in = tf.placeholder(shape=[None, o_size_h, o_size_w, c_channels], dtype=tf.float32,
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name='observation_0')
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streams = []
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for i in range(num_streams):
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self.conv1 = tf.layers.conv2d(self.observation_in, 16, kernel_size=[8, 8], strides=[4, 4],
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use_bias=False, activation=activation)
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self.conv2 = tf.layers.conv2d(self.conv1, 32, kernel_size=[4, 4], strides=[2, 2],
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use_bias=False, activation=activation)
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hidden = c_layers.flatten(self.conv2)
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for j in range(num_layers):
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hidden = tf.layers.dense(hidden, h_size, use_bias=False, activation=activation)
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streams.append(hidden)
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return streams
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def create_continuous_state_encoder(self, s_size, h_size, num_streams, activation, num_layers):
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"""
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Builds a set of hidden state encoders.
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:param s_size: state input size.
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:param h_size: Hidden layer size.
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:param num_streams: Number of state streams to construct.
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:param activation: What type of activation function to use for layers.
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:return: List of hidden layer tensors.
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"""
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self.state_in = tf.placeholder(shape=[None, s_size], dtype=tf.float32, name='state')
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if self.normalize:
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self.running_mean = tf.get_variable("running_mean", [s_size], trainable=False, dtype=tf.float32,
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initializer=tf.zeros_initializer())
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self.running_variance = tf.get_variable("running_variance", [s_size], trainable=False, dtype=tf.float32,
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initializer=tf.ones_initializer())
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self.normalized_state = tf.clip_by_value((self.state_in - self.running_mean) / tf.sqrt(
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self.running_variance / (tf.cast(self.global_step, tf.float32) + 1)), -5, 5, name="normalized_state")
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self.new_mean = tf.placeholder(shape=[s_size], dtype=tf.float32, name='new_mean')
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self.new_variance = tf.placeholder(shape=[s_size], dtype=tf.float32, name='new_variance')
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self.update_mean = tf.assign(self.running_mean, self.new_mean)
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self.update_variance = tf.assign(self.running_variance, self.new_variance)
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else:
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self.normalized_state = self.state_in
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streams = []
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for i in range(num_streams):
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hidden = self.normalized_state
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for j in range(num_layers):
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hidden = tf.layers.dense(hidden, h_size, use_bias=False, activation=activation)
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streams.append(hidden)
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return streams
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def create_discrete_state_encoder(self, s_size, h_size, num_streams, activation, num_layers):
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"""
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Builds a set of hidden state encoders from discrete state input.
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:param s_size: state input size (discrete).
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:param h_size: Hidden layer size.
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:param num_streams: Number of state streams to construct.
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:param activation: What type of activation function to use for layers.
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:return: List of hidden layer tensors.
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"""
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self.state_in = tf.placeholder(shape=[None, 1], dtype=tf.int32, name='state')
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state_in = tf.reshape(self.state_in, [-1])
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state_onehot = c_layers.one_hot_encoding(state_in, s_size)
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streams = []
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hidden = state_onehot
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for i in range(num_streams):
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for j in range(num_layers):
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hidden = tf.layers.dense(hidden, h_size, use_bias=False, activation=activation)
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streams.append(hidden)
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return streams
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def create_ppo_optimizer(self, probs, old_probs, value, entropy, beta, epsilon, lr, max_step):
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"""
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Creates training-specific Tensorflow ops for PPO models.
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:param probs: Current policy probabilities
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:param old_probs: Past policy probabilities
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:param value: Current value estimate
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:param beta: Entropy regularization strength
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:param entropy: Current policy entropy
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:param epsilon: Value for policy-divergence threshold
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:param lr: Learning rate
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:param max_step: Total number of training steps.
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"""
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self.returns_holder = tf.placeholder(shape=[None], dtype=tf.float32, name='discounted_rewards')
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self.advantage = tf.placeholder(shape=[None, 1], dtype=tf.float32, name='advantages')
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decay_epsilon = tf.train.polynomial_decay(epsilon, self.global_step,
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max_step, 1e-2,
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power=1.0)
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r_theta = probs / (old_probs + 1e-10)
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p_opt_a = r_theta * self.advantage
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p_opt_b = tf.clip_by_value(r_theta, 1 - decay_epsilon, 1 + decay_epsilon) * self.advantage
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self.policy_loss = -tf.reduce_mean(tf.minimum(p_opt_a, p_opt_b))
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self.value_loss = tf.reduce_mean(tf.squared_difference(self.returns_holder,
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tf.reduce_sum(value, axis=1)))
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decay_beta = tf.train.polynomial_decay(beta, self.global_step,
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max_step, 1e-5,
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power=1.0)
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self.loss = self.policy_loss + self.value_loss - decay_beta * tf.reduce_mean(entropy)
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self.learning_rate = tf.train.polynomial_decay(lr, self.global_step,
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max_step, 1e-10,
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power=1.0)
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optimizer = tf.train.AdamOptimizer(learning_rate=self.learning_rate)
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self.update_batch = optimizer.minimize(self.loss)
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class ContinuousControlModel(PPOModel):
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def __init__(self, lr, brain, h_size, epsilon, max_step, normalize, num_layers):
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"""
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Creates Continuous Control Actor-Critic model.
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:param brain: State-space size
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:param h_size: Hidden layer size
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"""
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super(ContinuousControlModel, self).__init__()
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s_size = brain.state_space_size
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a_size = brain.action_space_size
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self.normalize = normalize
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self.create_global_steps()
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self.create_reward_encoder()
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hidden_state, hidden_visual, hidden_policy, hidden_value = None, None, None, None
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if brain.number_observations > 0:
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height_size, width_size = brain.camera_resolutions[0]['height'], brain.camera_resolutions[0]['width']
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bw = brain.camera_resolutions[0]['blackAndWhite']
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hidden_visual = self.create_visual_encoder(height_size, width_size, bw, h_size, 2, tf.nn.tanh, num_layers)
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if brain.state_space_size > 0:
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s_size = brain.state_space_size
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if brain.state_space_type == "continuous":
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hidden_state = self.create_continuous_state_encoder(s_size, h_size, 2, tf.nn.tanh, num_layers)
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else:
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hidden_state = self.create_discrete_state_encoder(s_size, h_size, 2, tf.nn.tanh, num_layers)
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if hidden_visual is None and hidden_state is None:
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raise Exception("No valid network configuration possible. "
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"There are no states or observations in this brain")
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elif hidden_visual is not None and hidden_state is None:
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hidden_policy, hidden_value = hidden_visual
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elif hidden_visual is None and hidden_state is not None:
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hidden_policy, hidden_value = hidden_state
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elif hidden_visual is not None and hidden_state is not None:
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hidden_policy = tf.concat([hidden_visual[0], hidden_state[0]], axis=1)
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hidden_value = tf.concat([hidden_visual[1], hidden_state[1]], axis=1)
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self.batch_size = tf.placeholder(shape=None, dtype=tf.int32, name='batch_size')
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self.mu = tf.layers.dense(hidden_policy, a_size, activation=None, use_bias=False,
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kernel_initializer=c_layers.variance_scaling_initializer(factor=0.01))
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self.log_sigma_sq = tf.get_variable("log_sigma_squared", [a_size], dtype=tf.float32,
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initializer=tf.zeros_initializer())
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self.sigma_sq = tf.exp(self.log_sigma_sq)
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self.epsilon = tf.placeholder(shape=[None, a_size], dtype=tf.float32, name='epsilon')
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self.output = self.mu + tf.sqrt(self.sigma_sq) * self.epsilon
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self.output = tf.identity(self.output, name='action')
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a = tf.exp(-1 * tf.pow(tf.stop_gradient(self.output) - self.mu, 2) / (2 * self.sigma_sq))
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b = 1 / tf.sqrt(2 * self.sigma_sq * np.pi)
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self.probs = tf.multiply(a, b, name="action_probs")
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self.entropy = tf.reduce_sum(0.5 * tf.log(2 * np.pi * np.e * self.sigma_sq))
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self.value = tf.layers.dense(hidden_value, 1, activation=None, use_bias=False)
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self.value = tf.identity(self.value, name="value_estimate")
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self.old_probs = tf.placeholder(shape=[None, a_size], dtype=tf.float32, name='old_probabilities')
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self.create_ppo_optimizer(self.probs, self.old_probs, self.value, self.entropy, 0.0, epsilon, lr, max_step)
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class DiscreteControlModel(PPOModel):
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def __init__(self, lr, brain, h_size, epsilon, beta, max_step, normalize, num_layers):
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"""
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Creates Discrete Control Actor-Critic model.
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:param brain: State-space size
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:param h_size: Hidden layer size
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"""
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super(DiscreteControlModel, self).__init__()
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self.create_global_steps()
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self.create_reward_encoder()
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self.normalize = normalize
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hidden_state, hidden_visual, hidden = None, None, None
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if brain.number_observations > 0:
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height_size, width_size = brain.camera_resolutions[0]['height'], brain.camera_resolutions[0]['width']
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bw = brain.camera_resolutions[0]['blackAndWhite']
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hidden_visual = self.create_visual_encoder(height_size, width_size, bw, h_size, 1, tf.nn.elu, num_layers)[0]
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if brain.state_space_size > 0:
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s_size = brain.state_space_size
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if brain.state_space_type == "continuous":
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hidden_state = self.create_continuous_state_encoder(s_size, h_size, 1, tf.nn.elu, num_layers)[0]
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else:
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hidden_state = self.create_discrete_state_encoder(s_size, h_size, 1, tf.nn.elu, num_layers)[0]
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if hidden_visual is None and hidden_state is None:
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raise Exception("No valid network configuration possible. "
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"There are no states or observations in this brain")
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elif hidden_visual is not None and hidden_state is None:
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hidden = hidden_visual
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elif hidden_visual is None and hidden_state is not None:
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hidden = hidden_state
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elif hidden_visual is not None and hidden_state is not None:
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hidden = tf.concat([hidden_visual, hidden_state], axis=1)
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a_size = brain.action_space_size
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self.batch_size = tf.placeholder(shape=None, dtype=tf.int32, name='batch_size')
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self.policy = tf.layers.dense(hidden, a_size, activation=None, use_bias=False,
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kernel_initializer=c_layers.variance_scaling_initializer(factor=0.01))
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self.probs = tf.nn.softmax(self.policy, name="action_probs")
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self.output = tf.multinomial(self.policy, 1)
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self.output = tf.identity(self.output, name="action")
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self.value = tf.layers.dense(hidden, 1, activation=None, use_bias=False,
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kernel_initializer=c_layers.variance_scaling_initializer(factor=1.0))
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self.value = tf.identity(self.value, name="value_estimate")
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self.entropy = -tf.reduce_sum(self.probs * tf.log(self.probs + 1e-10), axis=1)
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self.action_holder = tf.placeholder(shape=[None], dtype=tf.int32)
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self.selected_actions = c_layers.one_hot_encoding(self.action_holder, a_size)
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self.old_probs = tf.placeholder(shape=[None, a_size], dtype=tf.float32, name='old_probabilities')
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self.responsible_probs = tf.reduce_sum(self.probs * self.selected_actions, axis=1)
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self.old_responsible_probs = tf.reduce_sum(self.old_probs * self.selected_actions, axis=1)
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self.create_ppo_optimizer(self.responsible_probs, self.old_responsible_probs,
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self.value, self.entropy, beta, epsilon, lr, max_step)
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