Unity 机器学习代理工具包 (ML-Agents) 是一个开源项目,它使游戏和模拟能够作为训练智能代理的环境。
您最多选择25个主题 主题必须以中文或者字母或数字开头,可以包含连字符 (-),并且长度不得超过35个字符
 
 
 
 
 

1081 行
40 KiB

import logging
import numpy as np
import tensorflow as tf
from mlagents.trainers.models import LearningModel, LearningRateSchedule, EncoderType
import tensorflow.contrib.layers as c_layers
LOG_STD_MAX = 2
LOG_STD_MIN = -20
EPSILON = 1e-6 # Small value to avoid divide by zero
DISCRETE_TARGET_ENTROPY_SCALE = 0.2 # Roughly equal to e-greedy 0.05
CONTINUOUS_TARGET_ENTROPY_SCALE = 1.0 # TODO: Make these an optional hyperparam.
LOGGER = logging.getLogger("mlagents.trainers")
POLICY_SCOPE = ""
TARGET_SCOPE = "target_network"
class SACNetwork(LearningModel):
"""
Base class for an SAC network. Implements methods for creating the actor and critic heads.
"""
def __init__(
self,
brain,
m_size=None,
h_size=128,
normalize=False,
use_recurrent=False,
num_layers=2,
stream_names=None,
seed=0,
vis_encode_type=EncoderType.SIMPLE,
):
LearningModel.__init__(
self, m_size, normalize, use_recurrent, brain, seed, stream_names
)
self.normalize = normalize
self.use_recurrent = use_recurrent
self.num_layers = num_layers
self.stream_names = stream_names
self.h_size = h_size
self.activ_fn = self.swish
def get_vars(self, scope):
return tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, scope=scope)
def join_scopes(self, scope_1, scope_2):
"""
Joins two scopes. Does so safetly (i.e., if one of the two scopes doesn't
exist, don't add any backslashes)
"""
if not scope_1:
return scope_2
if not scope_2:
return scope_1
else:
return "/".join(filter(None, [scope_1, scope_2]))
def create_cc_critic(self, hidden_value, scope, create_qs=True):
"""
Creates just the critic network
"""
scope = self.join_scopes(scope, "critic")
self.create_sac_value_head(
self.stream_names,
hidden_value,
self.num_layers,
self.h_size,
self.join_scopes(scope, "value"),
)
self.value_vars = self.get_vars(self.join_scopes(scope, "value"))
if create_qs:
hidden_q = tf.concat([hidden_value, self.external_action_in], axis=-1)
hidden_qp = tf.concat([hidden_value, self.output], axis=-1)
self.q1_heads, self.q2_heads, self.q1, self.q2 = self.create_q_heads(
self.stream_names,
hidden_q,
self.num_layers,
self.h_size,
self.join_scopes(scope, "q"),
)
self.q1_pheads, self.q2_pheads, self.q1_p, self.q2_p = self.create_q_heads(
self.stream_names,
hidden_qp,
self.num_layers,
self.h_size,
self.join_scopes(scope, "q"),
reuse=True,
)
self.q_vars = self.get_vars(self.join_scopes(scope, "q"))
self.critic_vars = self.get_vars(scope)
def create_dc_critic(self, hidden_value, scope, create_qs=True):
"""
Creates just the critic network
"""
scope = self.join_scopes(scope, "critic")
self.create_sac_value_head(
self.stream_names,
hidden_value,
self.num_layers,
self.h_size,
self.join_scopes(scope, "value"),
)
self.value_vars = self.get_vars("/".join([scope, "value"]))
if create_qs:
self.q1_heads, self.q2_heads, self.q1, self.q2 = self.create_q_heads(
self.stream_names,
hidden_value,
self.num_layers,
self.h_size,
self.join_scopes(scope, "q"),
num_outputs=sum(self.act_size),
)
self.q1_pheads, self.q2_pheads, self.q1_p, self.q2_p = self.create_q_heads(
self.stream_names,
hidden_value,
self.num_layers,
self.h_size,
self.join_scopes(scope, "q"),
reuse=True,
num_outputs=sum(self.act_size),
)
self.q_vars = self.get_vars(scope)
self.critic_vars = self.get_vars(scope)
def create_cc_actor(self, hidden_policy, scope):
"""
Creates Continuous control actor for SAC.
:param hidden_policy: Output of feature extractor (i.e. the input for vector obs, output of CNN for visual obs).
:param num_layers: TF scope to assign whatever is created in this block.
"""
# Create action input (continuous)
self.action_holder = tf.placeholder(
shape=[None, self.act_size[0]], dtype=tf.float32, name="action_holder"
)
self.external_action_in = self.action_holder
scope = self.join_scopes(scope, "policy")
with tf.variable_scope(scope):
hidden_policy = self.create_vector_observation_encoder(
hidden_policy,
self.h_size,
self.activ_fn,
self.num_layers,
"encoder",
False,
)
if self.use_recurrent:
hidden_policy, memory_out = self.create_recurrent_encoder(
hidden_policy,
self.policy_memory_in,
self.sequence_length,
name="lstm_policy",
)
self.policy_memory_out = memory_out
with tf.variable_scope(scope):
mu = tf.layers.dense(
hidden_policy,
self.act_size[0],
activation=None,
name="mu",
kernel_initializer=LearningModel.scaled_init(0.01),
)
# Policy-dependent log_sigma_sq
log_sigma_sq = tf.layers.dense(
hidden_policy,
self.act_size[0],
activation=None,
name="log_std",
kernel_initializer=LearningModel.scaled_init(0.01),
)
self.log_sigma_sq = tf.clip_by_value(log_sigma_sq, LOG_STD_MIN, LOG_STD_MAX)
sigma_sq = tf.exp(self.log_sigma_sq)
# Do the reparameterization trick
policy_ = mu + tf.random_normal(tf.shape(mu)) * sigma_sq
_gauss_pre = -0.5 * (
((policy_ - mu) / (tf.exp(self.log_sigma_sq) + EPSILON)) ** 2
+ 2 * self.log_sigma_sq
+ np.log(2 * np.pi)
)
all_probs = tf.reduce_sum(_gauss_pre, axis=1, keepdims=True)
self.entropy = tf.reduce_sum(
self.log_sigma_sq + 0.5 * np.log(2.0 * np.pi * np.e), axis=-1
)
# Squash probabilities
# Keep deterministic around in case we want to use it.
self.deterministic_output = tf.tanh(mu)
# Note that this is just for symmetry with PPO.
self.output_pre = tf.tanh(policy_)
# Squash correction
all_probs -= tf.reduce_sum(
tf.log(1 - self.output_pre ** 2 + EPSILON), axis=1, keepdims=True
)
self.all_log_probs = all_probs
self.selected_actions = tf.stop_gradient(self.output_pre)
self.action_probs = all_probs
# Extract output for Barracuda
self.output = tf.identity(self.output_pre, name="action")
# Get all policy vars
self.policy_vars = self.get_vars(scope)
def create_dc_actor(self, hidden_policy, scope):
"""
Creates Discrete control actor for SAC.
:param hidden_policy: Output of feature extractor (i.e. the input for vector obs, output of CNN for visual obs).
:param num_layers: TF scope to assign whatever is created in this block.
"""
scope = self.join_scopes(scope, "policy")
# Create inputs outside of the scope
self.action_masks = tf.placeholder(
shape=[None, sum(self.act_size)], dtype=tf.float32, name="action_masks"
)
if self.use_recurrent:
self.prev_action = tf.placeholder(
shape=[None, len(self.act_size)], dtype=tf.int32, name="prev_action"
)
with tf.variable_scope(scope):
hidden_policy = self.create_vector_observation_encoder(
hidden_policy,
self.h_size,
self.activ_fn,
self.num_layers,
"encoder",
False,
)
if self.use_recurrent:
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_policy = tf.concat([hidden_policy, prev_action_oh], axis=1)
hidden_policy, memory_out = self.create_recurrent_encoder(
hidden_policy,
self.policy_memory_in,
self.sequence_length,
name="lstm_policy",
)
self.policy_memory_out = memory_out
with tf.variable_scope(scope):
policy_branches = []
for size in self.act_size:
policy_branches.append(
tf.layers.dense(
hidden_policy,
size,
activation=None,
use_bias=False,
kernel_initializer=c_layers.variance_scaling_initializer(
factor=0.01
),
)
)
all_logits = tf.concat(
[branch for branch in policy_branches], axis=1, name="action_probs"
)
output, normalized_probs, normalized_logprobs = self.create_discrete_action_masking_layer(
all_logits, self.action_masks, self.act_size
)
self.action_probs = normalized_probs
# Really, this is entropy, but it has an analogous purpose to the log probs in the
# continuous case.
self.all_log_probs = self.action_probs * normalized_logprobs
self.output = output
# Create action input (discrete)
self.action_holder = tf.placeholder(
shape=[None, len(policy_branches)], dtype=tf.int32, name="action_holder"
)
self.output_oh = tf.concat(
[
tf.one_hot(self.action_holder[:, i], self.act_size[i])
for i in range(len(self.act_size))
],
axis=1,
)
# For Curiosity and GAIL to retrieve selected actions. We don't
# need the mask at this point because it's already stored in the buffer.
self.selected_actions = tf.stop_gradient(self.output_oh)
self.external_action_in = tf.concat(
[
tf.one_hot(self.action_holder[:, i], self.act_size[i])
for i in range(len(self.act_size))
],
axis=1,
)
# This is total entropy over all branches
self.entropy = -1 * tf.reduce_sum(self.all_log_probs, axis=1)
# Extract the normalized logprobs for Barracuda
self.normalized_logprobs = tf.identity(normalized_logprobs, name="action")
# We kept the LSTMs at a different scope than the rest, so add them if they exist.
self.policy_vars = self.get_vars(scope)
if self.use_recurrent:
self.policy_vars += self.get_vars("lstm")
def create_sac_value_head(
self, stream_names, hidden_input, num_layers, h_size, scope
):
"""
Creates one value estimator head for each reward signal in stream_names.
Also creates the node corresponding to the mean of all the value heads in self.value.
self.value_head is a dictionary of stream name to node containing the value estimator head for that signal.
:param stream_names: The list of reward signal names
:param hidden_input: The last layer of the Critic. The heads will consist of one dense hidden layer on top
of the hidden input.
:param num_layers: Number of hidden layers for value network
:param h_size: size of hidden layers for value network
:param scope: TF scope for value network.
"""
self.value_heads = {}
with tf.variable_scope(scope):
value_hidden = self.create_vector_observation_encoder(
hidden_input, h_size, self.activ_fn, num_layers, "encoder", False
)
if self.use_recurrent:
value_hidden, memory_out = self.create_recurrent_encoder(
value_hidden,
self.value_memory_in,
self.sequence_length,
name="lstm_value",
)
self.value_memory_out = memory_out
self.create_value_heads(stream_names, value_hidden)
def create_q_heads(
self,
stream_names,
hidden_input,
num_layers,
h_size,
scope,
reuse=False,
num_outputs=1,
):
"""
Creates two q heads for each reward signal in stream_names.
Also creates the node corresponding to the mean of all the value heads in self.value.
self.value_head is a dictionary of stream name to node containing the value estimator head for that signal.
:param stream_names: The list of reward signal names
:param hidden_input: The last layer of the Critic. The heads will consist of one dense hidden layer on top
of the hidden input.
:param num_layers: Number of hidden layers for Q network
:param h_size: size of hidden layers for Q network
:param scope: TF scope for Q network.
:param reuse: Whether or not to reuse variables. Useful for creating Q of policy.
:param num_outputs: Number of outputs of each Q function. If discrete, equal to number of actions.
"""
with tf.variable_scope(self.join_scopes(scope, "q1_encoding"), reuse=reuse):
q1_hidden = self.create_vector_observation_encoder(
hidden_input, h_size, self.activ_fn, num_layers, "q1_encoder", reuse
)
if self.use_recurrent:
q1_hidden, memory_out = self.create_recurrent_encoder(
q1_hidden, self.q1_memory_in, self.sequence_length, name="lstm_q1"
)
self.q1_memory_out = memory_out
q1_heads = {}
for name in stream_names:
_q1 = tf.layers.dense(q1_hidden, num_outputs, name="{}_q1".format(name))
q1_heads[name] = _q1
q1 = tf.reduce_mean(list(q1_heads.values()), axis=0)
with tf.variable_scope(self.join_scopes(scope, "q2_encoding"), reuse=reuse):
q2_hidden = self.create_vector_observation_encoder(
hidden_input, h_size, self.activ_fn, num_layers, "q2_encoder", reuse
)
if self.use_recurrent:
q2_hidden, memory_out = self.create_recurrent_encoder(
q2_hidden, self.q2_memory_in, self.sequence_length, name="lstm_q2"
)
self.q2_memory_out = memory_out
q2_heads = {}
for name in stream_names:
_q2 = tf.layers.dense(q2_hidden, num_outputs, name="{}_q2".format(name))
q2_heads[name] = _q2
q2 = tf.reduce_mean(list(q2_heads.values()), axis=0)
return q1_heads, q2_heads, q1, q2
def copy_normalization(self, mean, variance, steps):
"""
Copies the mean, variance, and steps into the normalizers of the
input of this SACNetwork. Used to copy the normalizer from the policy network
to the target network.
param mean: Tensor containing the mean.
param variance: Tensor containing the variance
param steps: Tensor containing the number of steps.
"""
update_mean = tf.assign(self.running_mean, mean)
update_variance = tf.assign(self.running_variance, variance)
update_norm_step = tf.assign(self.normalization_steps, steps)
return tf.group([update_mean, update_variance, update_norm_step])
class SACTargetNetwork(SACNetwork):
"""
Instantiation for the SAC target network. Only contains a single
value estimator and is updated from the Policy Network.
"""
def __init__(
self,
brain,
m_size=None,
h_size=128,
normalize=False,
use_recurrent=False,
num_layers=2,
stream_names=None,
seed=0,
vis_encode_type=EncoderType.SIMPLE,
):
super().__init__(
brain,
m_size,
h_size,
normalize,
use_recurrent,
num_layers,
stream_names,
seed,
vis_encode_type,
)
if self.use_recurrent:
self.memory_in = tf.placeholder(
shape=[None, self.m_size], dtype=tf.float32, name="recurrent_in"
)
self.value_memory_in = self.memory_in
with tf.variable_scope(TARGET_SCOPE):
hidden_streams = self.create_observation_streams(
1,
self.h_size,
0,
vis_encode_type=vis_encode_type,
stream_scopes=["critic/value/"],
)
if brain.vector_action_space_type == "continuous":
self.create_cc_critic(hidden_streams[0], TARGET_SCOPE, create_qs=False)
else:
self.create_dc_critic(hidden_streams[0], TARGET_SCOPE, create_qs=False)
if self.use_recurrent:
self.memory_out = tf.concat(
self.value_memory_out, axis=1
) # Needed for Barracuda to work
class SACPolicyNetwork(SACNetwork):
"""
Instantiation for SAC policy network. Contains a dual Q estimator,
a value estimator, and the actual policy network.
"""
def __init__(
self,
brain,
m_size=None,
h_size=128,
normalize=False,
use_recurrent=False,
num_layers=2,
stream_names=None,
seed=0,
vis_encode_type=EncoderType.SIMPLE,
):
super().__init__(
brain,
m_size,
h_size,
normalize,
use_recurrent,
num_layers,
stream_names,
seed,
vis_encode_type,
)
self.share_ac_cnn = False
if self.use_recurrent:
self.create_memory_ins(self.m_size)
hidden_policy, hidden_critic = self.create_observation_ins(
vis_encode_type, self.share_ac_cnn
)
if brain.vector_action_space_type == "continuous":
self.create_cc_actor(hidden_policy, POLICY_SCOPE)
self.create_cc_critic(hidden_critic, POLICY_SCOPE)
else:
self.create_dc_actor(hidden_policy, POLICY_SCOPE)
self.create_dc_critic(hidden_critic, POLICY_SCOPE)
if self.share_ac_cnn:
# Make sure that the policy also contains the CNN
self.policy_vars += self.get_vars(
self.join_scopes(POLICY_SCOPE, "critic/value/main_graph_0_encoder0")
)
if self.use_recurrent:
mem_outs = [
self.value_memory_out,
self.q1_memory_out,
self.q2_memory_out,
self.policy_memory_out,
]
self.memory_out = tf.concat(mem_outs, axis=1)
def create_memory_ins(self, m_size):
"""
Creates the memory input placeholders for LSTM.
:param m_size: the total size of the memory.
"""
# Create the Policy input separate from the rest
# This is so in inference we only have to run the Policy network.
# Barracuda will grab the recurrent_in and recurrent_out named tensors.
self.inference_memory_in = tf.placeholder(
shape=[None, m_size // 4], dtype=tf.float32, name="recurrent_in"
)
# We assume m_size is divisible by 4
# Create the non-Policy inputs
# Use a default placeholder here so nothing has to be provided during
# Barracuda inference. Note that the default value is just the tiled input
# for the policy, which is thrown away.
three_fourths_m_size = m_size * 3 // 4
self.other_memory_in = tf.placeholder_with_default(
input=tf.tile(self.inference_memory_in, [1, 3]),
shape=[None, three_fourths_m_size],
name="other_recurrent_in",
)
# Concat and use this as the "placeholder"
# for training
self.memory_in = tf.concat(
[self.other_memory_in, self.inference_memory_in], axis=1
)
# Re-break-up for each network
num_mems = 4
mem_ins = []
for i in range(num_mems):
_start = m_size // num_mems * i
_end = m_size // num_mems * (i + 1)
mem_ins.append(self.memory_in[:, _start:_end])
self.value_memory_in = mem_ins[0]
self.q1_memory_in = mem_ins[1]
self.q2_memory_in = mem_ins[2]
self.policy_memory_in = mem_ins[3]
def create_observation_ins(self, vis_encode_type, share_ac_cnn):
"""
Creates the observation inputs, and a CNN if needed,
:param vis_encode_type: Type of CNN encoder.
:param share_ac_cnn: Whether or not to share the actor and critic CNNs.
:return A tuple of (hidden_policy, hidden_critic). We don't save it to self since they're used
once and thrown away.
"""
if share_ac_cnn:
with tf.variable_scope(POLICY_SCOPE):
hidden_streams = self.create_observation_streams(
1,
self.h_size,
0,
vis_encode_type=vis_encode_type,
stream_scopes=["critic/value/"],
)
hidden_policy = hidden_streams[0]
hidden_critic = hidden_streams[0]
else:
with tf.variable_scope(POLICY_SCOPE):
hidden_streams = self.create_observation_streams(
2,
self.h_size,
0,
vis_encode_type=vis_encode_type,
stream_scopes=["policy/", "critic/value/"],
)
hidden_policy = hidden_streams[0]
hidden_critic = hidden_streams[1]
return hidden_policy, hidden_critic
class SACModel(LearningModel):
def __init__(
self,
brain,
lr=1e-4,
lr_schedule=LearningRateSchedule.CONSTANT,
h_size=128,
init_entcoef=0.1,
max_step=5e6,
normalize=False,
use_recurrent=False,
num_layers=2,
m_size=None,
seed=0,
stream_names=None,
tau=0.005,
gammas=None,
vis_encode_type=EncoderType.SIMPLE,
):
"""
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 lr_schedule: Learning rate decay schedule.
:param h_size: Size of hidden layers
:param init_entcoef: Initial value for entropy coefficient. Set lower to learn faster,
set higher to explore more.
: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
:param tau: Strength of soft-Q update.
:param m_size: Size of brain memory.
"""
self.tau = tau
self.gammas = gammas
self.brain = brain
self.init_entcoef = init_entcoef
if stream_names is None:
stream_names = []
# Use to reduce "survivor bonus" when using Curiosity or GAIL.
self.use_dones_in_backup = {name: tf.Variable(1.0) for name in stream_names}
self.disable_use_dones = {
name: self.use_dones_in_backup[name].assign(0.0) for name in stream_names
}
LearningModel.__init__(
self, m_size, normalize, use_recurrent, brain, seed, stream_names
)
if num_layers < 1:
num_layers = 1
self.policy_network = SACPolicyNetwork(
brain=brain,
m_size=m_size,
h_size=h_size,
normalize=normalize,
use_recurrent=use_recurrent,
num_layers=num_layers,
seed=seed,
stream_names=stream_names,
vis_encode_type=vis_encode_type,
)
self.target_network = SACTargetNetwork(
brain=brain,
m_size=m_size // 4 if m_size else None,
h_size=h_size,
normalize=normalize,
use_recurrent=use_recurrent,
num_layers=num_layers,
seed=seed,
stream_names=stream_names,
vis_encode_type=vis_encode_type,
)
self.create_inputs_and_outputs()
self.learning_rate = self.create_learning_rate(
lr_schedule, lr, self.global_step, max_step
)
self.create_losses(
self.policy_network.q1_heads,
self.policy_network.q2_heads,
lr,
max_step,
stream_names,
discrete=self.brain.vector_action_space_type == "discrete",
)
self.selected_actions = (
self.policy_network.selected_actions
) # For GAIL and other reward signals
if normalize:
target_update_norm = self.target_network.copy_normalization(
self.policy_network.running_mean,
self.policy_network.running_variance,
self.policy_network.normalization_steps,
)
self.update_normalization = tf.group(
[self.policy_network.update_normalization, target_update_norm]
)
self.running_mean = self.policy_network.running_mean
self.running_variance = self.policy_network.running_variance
self.normalization_steps = self.policy_network.normalization_steps
def create_inputs_and_outputs(self):
"""
Assign the higher-level SACModel's inputs and outputs to those of its policy or
target network.
"""
self.vector_in = self.policy_network.vector_in
self.visual_in = self.policy_network.visual_in
self.next_vector_in = self.target_network.vector_in
self.next_visual_in = self.target_network.visual_in
self.action_holder = self.policy_network.action_holder
self.sequence_length = self.policy_network.sequence_length
self.next_sequence_length = self.target_network.sequence_length
if self.brain.vector_action_space_type == "discrete":
self.action_masks = self.policy_network.action_masks
else:
self.output_pre = self.policy_network.output_pre
self.output = self.policy_network.output
# Don't use value estimate during inference. TODO: Check why PPO uses value_estimate in inference.
self.value = tf.identity(
self.policy_network.value, name="value_estimate_unused"
)
self.value_heads = self.policy_network.value_heads
self.all_log_probs = self.policy_network.all_log_probs
self.dones_holder = tf.placeholder(
shape=[None], dtype=tf.float32, name="dones_holder"
)
# This is just a dummy to get pretraining to work. PPO has this but SAC doesn't.
# TODO: Proper input and output specs for models
self.epsilon = tf.placeholder(
shape=[None, self.act_size[0]], dtype=tf.float32, name="epsilon"
)
if self.use_recurrent:
self.memory_in = self.policy_network.memory_in
self.memory_out = self.policy_network.memory_out
# For Barracuda
self.inference_memory_out = tf.identity(
self.policy_network.policy_memory_out, name="recurrent_out"
)
if self.brain.vector_action_space_type == "discrete":
self.prev_action = self.policy_network.prev_action
self.next_memory_in = self.target_network.memory_in
def create_losses(
self, q1_streams, q2_streams, lr, max_step, stream_names, discrete=False
):
"""
Creates training-specific Tensorflow ops for SAC models.
:param q1_streams: Q1 streams from policy network
:param q1_streams: Q2 streams from policy network
:param lr: Learning rate
:param max_step: Total number of training steps.
:param stream_names: List of reward stream names.
:param discrete: Whether or not to use discrete action losses.
"""
if discrete:
self.target_entropy = [
DISCRETE_TARGET_ENTROPY_SCALE * np.log(i).astype(np.float32)
for i in self.act_size
]
else:
self.target_entropy = (
-1
* CONTINUOUS_TARGET_ENTROPY_SCALE
* np.prod(self.act_size[0]).astype(np.float32)
)
self.rewards_holders = {}
self.min_policy_qs = {}
for i, name in enumerate(stream_names):
if discrete:
_branched_mpq1 = self.apply_as_branches(
self.policy_network.q1_pheads[name]
* self.policy_network.action_probs
)
branched_mpq1 = tf.stack(
[
tf.reduce_sum(_br, axis=1, keep_dims=True)
for _br in _branched_mpq1
]
)
_q1_p_mean = tf.reduce_mean(branched_mpq1, axis=0)
_branched_mpq2 = self.apply_as_branches(
self.policy_network.q2_pheads[name]
* self.policy_network.action_probs
)
branched_mpq2 = tf.stack(
[
tf.reduce_sum(_br, axis=1, keep_dims=True)
for _br in _branched_mpq2
]
)
_q2_p_mean = tf.reduce_mean(branched_mpq2, axis=0)
self.min_policy_qs[name] = tf.minimum(_q1_p_mean, _q2_p_mean)
else:
self.min_policy_qs[name] = tf.minimum(
self.policy_network.q1_pheads[name],
self.policy_network.q2_pheads[name],
)
rewards_holder = tf.placeholder(
shape=[None], dtype=tf.float32, name="{}_rewards".format(name)
)
rewards_holder = tf.placeholder(
shape=[None], dtype=tf.float32, name="{}_rewards".format(name)
)
self.rewards_holders[name] = rewards_holder
q1_losses = []
q2_losses = []
# Multiple q losses per stream
expanded_dones = tf.expand_dims(self.dones_holder, axis=-1)
for i, name in enumerate(stream_names):
_expanded_rewards = tf.expand_dims(self.rewards_holders[name], axis=-1)
q_backup = tf.stop_gradient(
_expanded_rewards
+ (1.0 - self.use_dones_in_backup[name] * expanded_dones)
* self.gammas[i]
* self.target_network.value_heads[name]
)
if discrete:
# We need to break up the Q functions by branch, and update them individually.
branched_q1_stream = self.apply_as_branches(
self.policy_network.external_action_in * q1_streams[name]
)
branched_q2_stream = self.apply_as_branches(
self.policy_network.external_action_in * q2_streams[name]
)
# Reduce each branch into scalar
branched_q1_stream = [
tf.reduce_sum(_branch, axis=1, keep_dims=True)
for _branch in branched_q1_stream
]
branched_q2_stream = [
tf.reduce_sum(_branch, axis=1, keep_dims=True)
for _branch in branched_q2_stream
]
q1_stream = tf.reduce_mean(branched_q1_stream, axis=0)
q2_stream = tf.reduce_mean(branched_q2_stream, axis=0)
else:
q1_stream = q1_streams[name]
q2_stream = q2_streams[name]
_q1_loss = 0.5 * tf.reduce_mean(
tf.to_float(self.mask) * tf.squared_difference(q_backup, q1_stream)
)
_q2_loss = 0.5 * tf.reduce_mean(
tf.to_float(self.mask) * tf.squared_difference(q_backup, q2_stream)
)
q1_losses.append(_q1_loss)
q2_losses.append(_q2_loss)
self.q1_loss = tf.reduce_mean(q1_losses)
self.q2_loss = tf.reduce_mean(q2_losses)
# Learn entropy coefficient
if discrete:
# Create a log_ent_coef for each branch
self.log_ent_coef = tf.get_variable(
"log_ent_coef",
dtype=tf.float32,
initializer=np.log([self.init_entcoef] * len(self.act_size)).astype(
np.float32
),
trainable=True,
)
else:
self.log_ent_coef = tf.get_variable(
"log_ent_coef",
dtype=tf.float32,
initializer=np.log(self.init_entcoef).astype(np.float32),
trainable=True,
)
self.ent_coef = tf.exp(self.log_ent_coef)
if discrete:
# We also have to do a different entropy and target_entropy per branch.
branched_log_probs = self.apply_as_branches(
self.policy_network.all_log_probs
)
branched_ent_sums = tf.stack(
[
tf.reduce_sum(_lp, axis=1, keep_dims=True) + _te
for _lp, _te in zip(branched_log_probs, self.target_entropy)
],
axis=1,
)
self.entropy_loss = -tf.reduce_mean(
tf.to_float(self.mask)
* tf.reduce_mean(
self.log_ent_coef
* tf.squeeze(tf.stop_gradient(branched_ent_sums), axis=2),
axis=1,
)
)
# Same with policy loss, we have to do the loss per branch and average them,
# so that larger branches don't get more weight.
# The equivalent KL divergence from Eq 10 of Haarnoja et al. is also pi*log(pi) - Q
branched_q_term = self.apply_as_branches(
self.policy_network.action_probs * self.policy_network.q1_p
)
branched_policy_loss = tf.stack(
[
tf.reduce_sum(self.ent_coef[i] * _lp - _qt, axis=1, keep_dims=True)
for i, (_lp, _qt) in enumerate(
zip(branched_log_probs, branched_q_term)
)
]
)
self.policy_loss = tf.reduce_mean(
tf.to_float(self.mask) * tf.squeeze(branched_policy_loss)
)
# Do vbackup entropy bonus per branch as well.
branched_ent_bonus = tf.stack(
[
tf.reduce_sum(self.ent_coef[i] * _lp, axis=1, keep_dims=True)
for i, _lp in enumerate(branched_log_probs)
]
)
value_losses = []
for name in stream_names:
v_backup = tf.stop_gradient(
self.min_policy_qs[name]
- tf.reduce_mean(branched_ent_bonus, axis=0)
)
value_losses.append(
0.5
* tf.reduce_mean(
tf.to_float(self.mask)
* tf.squared_difference(
self.policy_network.value_heads[name], v_backup
)
)
)
else:
self.entropy_loss = -tf.reduce_mean(
self.log_ent_coef
* tf.to_float(self.mask)
* tf.stop_gradient(
tf.reduce_sum(
self.policy_network.all_log_probs + self.target_entropy,
axis=1,
keep_dims=True,
)
)
)
batch_policy_loss = tf.reduce_mean(
self.ent_coef * self.policy_network.all_log_probs
- self.policy_network.q1_p,
axis=1,
)
self.policy_loss = tf.reduce_mean(
tf.to_float(self.mask) * batch_policy_loss
)
value_losses = []
for name in stream_names:
v_backup = tf.stop_gradient(
self.min_policy_qs[name]
- tf.reduce_sum(
self.ent_coef * self.policy_network.all_log_probs, axis=1
)
)
value_losses.append(
0.5
* tf.reduce_mean(
tf.to_float(self.mask)
* tf.squared_difference(
self.policy_network.value_heads[name], v_backup
)
)
)
self.value_loss = tf.reduce_mean(value_losses)
self.total_value_loss = self.q1_loss + self.q2_loss + self.value_loss
self.entropy = self.policy_network.entropy
def apply_as_branches(self, concat_logits):
"""
Takes in a concatenated set of logits and breaks it up into a list of non-concatenated logits, one per
action branch
"""
action_idx = [0] + list(np.cumsum(self.act_size))
branches_logits = [
concat_logits[:, action_idx[i] : action_idx[i + 1]]
for i in range(len(self.act_size))
]
return branches_logits
def create_sac_optimizers(self):
"""
Creates the Adam optimizers and update ops for SAC, including
the policy, value, and entropy updates, as well as the target network update.
"""
policy_optimizer = tf.train.AdamOptimizer(learning_rate=self.learning_rate)
entropy_optimizer = tf.train.AdamOptimizer(learning_rate=self.learning_rate)
value_optimizer = tf.train.AdamOptimizer(learning_rate=self.learning_rate)
self.target_update_op = [
tf.assign(target, (1 - self.tau) * target + self.tau * source)
for target, source in zip(
self.target_network.value_vars, self.policy_network.value_vars
)
]
LOGGER.debug("value_vars")
self.print_all_vars(self.policy_network.value_vars)
LOGGER.debug("targvalue_vars")
self.print_all_vars(self.target_network.value_vars)
LOGGER.debug("critic_vars")
self.print_all_vars(self.policy_network.critic_vars)
LOGGER.debug("q_vars")
self.print_all_vars(self.policy_network.q_vars)
LOGGER.debug("policy_vars")
self.print_all_vars(self.policy_network.policy_vars)
self.target_init_op = [
tf.assign(target, source)
for target, source in zip(
self.target_network.value_vars, self.policy_network.value_vars
)
]
self.update_batch_policy = policy_optimizer.minimize(
self.policy_loss, var_list=self.policy_network.policy_vars
)
# Make sure policy is updated first, then value, then entropy.
with tf.control_dependencies([self.update_batch_policy]):
self.update_batch_value = value_optimizer.minimize(
self.total_value_loss, var_list=self.policy_network.critic_vars
)
# Add entropy coefficient optimization operation
with tf.control_dependencies([self.update_batch_value]):
self.update_batch_entropy = entropy_optimizer.minimize(
self.entropy_loss, var_list=self.log_ent_coef
)
def print_all_vars(self, variables):
for _var in variables:
LOGGER.debug(_var)