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。
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from typing import Dict, Optional
from mlagents.tf_utils import tf
from mlagents.trainers.models import ModelUtils, EncoderType
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.
POLICY_SCOPE = ""
TARGET_SCOPE = "target_network"
class SACNetwork:
"""
Base class for an SAC network. Implements methods for creating the actor and critic heads.
"""
def __init__(
self,
policy=None,
m_size=None,
h_size=128,
normalize=False,
use_recurrent=False,
num_layers=2,
stream_names=None,
vis_encode_type=EncoderType.SIMPLE,
):
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 = ModelUtils.swish
self.sequence_length_ph = tf.placeholder(
shape=None, dtype=tf.int32, name="sac_sequence_length"
)
self.policy_memory_in: Optional[tf.Tensor] = None
self.policy_memory_out: Optional[tf.Tensor] = None
self.value_memory_in: Optional[tf.Tensor] = None
self.value_memory_out: Optional[tf.Tensor] = None
self.q1: Optional[tf.Tensor] = None
self.q2: Optional[tf.Tensor] = None
self.q1_p: Optional[tf.Tensor] = None
self.q2_p: Optional[tf.Tensor] = None
self.q1_memory_in: Optional[tf.Tensor] = None
self.q2_memory_in: Optional[tf.Tensor] = None
self.q1_memory_out: Optional[tf.Tensor] = None
self.q2_memory_out: Optional[tf.Tensor] = None
self.prev_action: Optional[tf.Tensor] = None
self.action_masks: Optional[tf.Tensor] = None
self.external_action_in: Optional[tf.Tensor] = None
self.log_sigma_sq: Optional[tf.Tensor] = None
self.entropy: Optional[tf.Tensor] = None
self.deterministic_output: Optional[tf.Tensor] = None
self.normalized_logprobs: Optional[tf.Tensor] = None
self.action_probs: Optional[tf.Tensor] = None
self.output_oh: Optional[tf.Tensor] = None
self.output_pre: Optional[tf.Tensor] = None
self.value_vars = None
self.q_vars = None
self.critic_vars = None
self.policy_vars = None
self.q1_heads: Dict[str, tf.Tensor] = None
self.q2_heads: Dict[str, tf.Tensor] = None
self.q1_pheads: Dict[str, tf.Tensor] = None
self.q2_pheads: Dict[str, tf.Tensor] = None
self.policy = policy
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_value_heads(self, stream_names, hidden_input):
"""
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.
"""
self.value_heads = {}
for name in stream_names:
value = tf.layers.dense(hidden_input, 1, name="{}_value".format(name))
self.value_heads[name] = value
self.value = tf.reduce_mean(list(self.value_heads.values()), 0)
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.external_action_in = tf.placeholder(
shape=[None, self.policy.act_size[0]],
dtype=tf.float32,
name="external_action_in",
)
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.policy.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.policy.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.policy.act_size),
)
self.q_vars = self.get_vars(scope)
self.critic_vars = self.get_vars(scope)
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.
"""
with tf.variable_scope(scope):
value_hidden = ModelUtils.create_vector_observation_encoder(
hidden_input, h_size, self.activ_fn, num_layers, "encoder", False
)
if self.use_recurrent:
value_hidden, memory_out = ModelUtils.create_recurrent_encoder(
value_hidden,
self.value_memory_in,
self.sequence_length_ph,
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 = ModelUtils.create_vector_observation_encoder(
hidden_input, h_size, self.activ_fn, num_layers, "q1_encoder", reuse
)
if self.use_recurrent:
q1_hidden, memory_out = ModelUtils.create_recurrent_encoder(
q1_hidden,
self.q1_memory_in,
self.sequence_length_ph,
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 = ModelUtils.create_vector_observation_encoder(
hidden_input, h_size, self.activ_fn, num_layers, "q2_encoder", reuse
)
if self.use_recurrent:
q2_hidden, memory_out = ModelUtils.create_recurrent_encoder(
q2_hidden,
self.q2_memory_in,
self.sequence_length_ph,
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
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,
policy,
m_size=None,
h_size=128,
normalize=False,
use_recurrent=False,
num_layers=2,
stream_names=None,
vis_encode_type=EncoderType.SIMPLE,
):
super().__init__(
policy,
m_size,
h_size,
normalize,
use_recurrent,
num_layers,
stream_names,
vis_encode_type,
)
with tf.variable_scope(TARGET_SCOPE):
self.vector_in, self.visual_in = ModelUtils.create_input_placeholders(
self.policy.behavior_spec.observation_shapes
)
if self.policy.normalize:
normalization_tensors = ModelUtils.create_normalizer(self.vector_in)
self.update_normalization_op = normalization_tensors.update_op
self.normalization_steps = normalization_tensors.steps
self.running_mean = normalization_tensors.running_mean
self.running_variance = normalization_tensors.running_variance
self.processed_vector_in = ModelUtils.normalize_vector_obs(
self.vector_in,
self.running_mean,
self.running_variance,
self.normalization_steps,
)
else:
self.processed_vector_in = self.vector_in
self.update_normalization_op = None
if self.policy.use_recurrent:
self.memory_in = tf.placeholder(
shape=[None, m_size], dtype=tf.float32, name="target_recurrent_in"
)
self.value_memory_in = self.memory_in
hidden_streams = ModelUtils.create_observation_streams(
self.visual_in,
self.processed_vector_in,
1,
self.h_size,
0,
vis_encode_type=vis_encode_type,
stream_scopes=["critic/value/"],
)
if self.policy.use_continuous_act:
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
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 SACPolicyNetwork(SACNetwork):
"""
Instantiation for SAC policy network. Contains a dual Q estimator,
a value estimator, and a reference to the actual policy network.
"""
def __init__(
self,
policy,
m_size=None,
h_size=128,
normalize=False,
use_recurrent=False,
num_layers=2,
stream_names=None,
vis_encode_type=EncoderType.SIMPLE,
):
super().__init__(
policy,
m_size,
h_size,
normalize,
use_recurrent,
num_layers,
stream_names,
vis_encode_type,
)
if self.policy.use_recurrent:
self._create_memory_ins(m_size)
hidden_critic = self._create_observation_in(vis_encode_type)
self.policy.output = self.policy.output
# Use the sequence length of the policy
self.sequence_length_ph = self.policy.sequence_length_ph
if self.policy.use_continuous_act:
self._create_cc_critic(hidden_critic, POLICY_SCOPE)
else:
self._create_dc_critic(hidden_critic, POLICY_SCOPE)
if self.use_recurrent:
mem_outs = [self.value_memory_out, self.q1_memory_out, self.q2_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.
"""
self.memory_in = tf.placeholder(
shape=[None, m_size * 3], dtype=tf.float32, name="value_recurrent_in"
)
# Re-break-up for each network
num_mems = 3
input_size = self.memory_in.get_shape().as_list()[1]
mem_ins = []
for i in range(num_mems):
_start = input_size // num_mems * i
_end = input_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]
def _create_observation_in(self, vis_encode_type):
"""
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.
"""
with tf.variable_scope(POLICY_SCOPE):
hidden_streams = ModelUtils.create_observation_streams(
self.policy.visual_in,
self.policy.processed_vector_in,
1,
self.h_size,
0,
vis_encode_type=vis_encode_type,
stream_scopes=["critic/value/"],
)
hidden_critic = hidden_streams[0]
return hidden_critic