# Source code for stable_baselines.sac.policies

import tensorflow as tf
import numpy as np
from gym.spaces import Box

from stable_baselines.common.policies import BasePolicy, nature_cnn, register_policy
from stable_baselines.common.tf_layers import mlp

EPS = 1e-6  # Avoid NaN (prevents division by zero or log of zero)
# CAP the standard deviation of the actor
LOG_STD_MAX = 2
LOG_STD_MIN = -20

def gaussian_likelihood(input_, mu_, log_std):
"""
Helper to computer log likelihood of a gaussian.
Here we assume this is a Diagonal Gaussian.

:param input_: (tf.Tensor)
:param mu_: (tf.Tensor)
:param log_std: (tf.Tensor)
:return: (tf.Tensor)
"""
pre_sum = -0.5 * (((input_ - mu_) / (tf.exp(log_std) + EPS)) ** 2 + 2 * log_std + np.log(2 * np.pi))
return tf.reduce_sum(pre_sum, axis=1)

def gaussian_entropy(log_std):
"""
Compute the entropy for a diagonal Gaussian distribution.

:param log_std: (tf.Tensor) Log of the standard deviation
:return: (tf.Tensor)
"""
return tf.reduce_sum(log_std + 0.5 * np.log(2.0 * np.pi * np.e), axis=-1)

clip_up = tf.cast(input_ > upper, tf.float32)
clip_low = tf.cast(input_ < lower, tf.float32)
return input_ + tf.stop_gradient((upper - input_) * clip_up + (lower - input_) * clip_low)

def apply_squashing_func(mu_, pi_, logp_pi):
"""
Squash the output of the Gaussian distribution
and account for that in the log probability
The squashed mean is also returned for using
deterministic actions.

:param mu_: (tf.Tensor) Mean of the gaussian
:param pi_: (tf.Tensor) Output of the policy before squashing
:param logp_pi: (tf.Tensor) Log probability before squashing
:return: ([tf.Tensor])
"""
# Squash the output
deterministic_policy = tf.tanh(mu_)
policy = tf.tanh(pi_)
# OpenAI Variation:
# To avoid evil machine precision error, strictly clip 1-pi**2 to [0,1] range.
# logp_pi -= tf.reduce_sum(tf.log(clip_but_pass_gradient(1 - policy ** 2, lower=0, upper=1) + EPS), axis=1)
# Squash correction (from original implementation)
logp_pi -= tf.reduce_sum(tf.log(1 - policy ** 2 + EPS), axis=1)
return deterministic_policy, policy, logp_pi

class SACPolicy(BasePolicy):
"""
Policy object that implements a SAC-like actor critic

:param sess: (TensorFlow session) The current TensorFlow session
:param ob_space: (Gym Space) The observation space of the environment
:param ac_space: (Gym Space) The action space of the environment
:param n_env: (int) The number of environments to run
:param n_steps: (int) The number of steps to run for each environment
:param n_batch: (int) The number of batch to run (n_envs * n_steps)
:param reuse: (bool) If the policy is reusable or not
:param scale: (bool) whether or not to scale the input
"""

def __init__(self, sess, ob_space, ac_space, n_env=1, n_steps=1, n_batch=None, reuse=False, scale=False):
super(SACPolicy, self).__init__(sess, ob_space, ac_space, n_env, n_steps, n_batch, reuse=reuse, scale=scale)
assert isinstance(ac_space, Box), "Error: the action space must be of type gym.spaces.Box"

self.qf1 = None
self.qf2 = None
self.value_fn = None
self.policy = None
self.deterministic_policy = None
self.act_mu = None
self.std = None

def make_actor(self, obs=None, reuse=False, scope="pi"):
"""
Creates an actor object

:param obs: (TensorFlow Tensor) The observation placeholder (can be None for default placeholder)
:param reuse: (bool) whether or not to reuse parameters
:param scope: (str) the scope name of the actor
:return: (TensorFlow Tensor) the output tensor
"""
raise NotImplementedError

def make_critics(self, obs=None, action=None, reuse=False,
scope="values_fn", create_vf=True, create_qf=True):
"""
Creates the two Q-Values approximator along with the Value function

:param obs: (TensorFlow Tensor) The observation placeholder (can be None for default placeholder)
:param action: (TensorFlow Tensor) The action placeholder
:param reuse: (bool) whether or not to reuse parameters
:param scope: (str) the scope name
:param create_vf: (bool) Whether to create Value fn or not
:param create_qf: (bool) Whether to create Q-Values fn or not
:return: ([tf.Tensor]) Mean, action and log probability
"""
raise NotImplementedError

def step(self, obs, state=None, mask=None, deterministic=False):
"""
Returns the policy for a single step

:param obs: ([float] or [int]) The current observation of the environment
:param state: ([float]) The last states (used in recurrent policies)
:param deterministic: (bool) Whether or not to return deterministic actions.
:return: ([float]) actions
"""
raise NotImplementedError

"""
Returns the action probability params (mean, std) for a single step

:param obs: ([float] or [int]) The current observation of the environment
:param state: ([float]) The last states (used in recurrent policies)
:return: ([float], [float])
"""
raise NotImplementedError

class FeedForwardPolicy(SACPolicy):
"""
Policy object that implements a DDPG-like actor critic, using a feed forward neural network.

:param sess: (TensorFlow session) The current TensorFlow session
:param ob_space: (Gym Space) The observation space of the environment
:param ac_space: (Gym Space) The action space of the environment
:param n_env: (int) The number of environments to run
:param n_steps: (int) The number of steps to run for each environment
:param n_batch: (int) The number of batch to run (n_envs * n_steps)
:param reuse: (bool) If the policy is reusable or not
:param layers: ([int]) The size of the Neural network for the policy (if None, default to [64, 64])
:param cnn_extractor: (function (TensorFlow Tensor, **kwargs): (TensorFlow Tensor)) the CNN feature extraction
:param feature_extraction: (str) The feature extraction type ("cnn" or "mlp")
:param layer_norm: (bool) enable layer normalisation
:param reg_weight: (float) Regularization loss weight for the policy parameters
:param act_fun: (tf.func) the activation function to use in the neural network.
:param kwargs: (dict) Extra keyword arguments for the nature CNN feature extraction
"""

def __init__(self, sess, ob_space, ac_space, n_env=1, n_steps=1, n_batch=None, reuse=False, layers=None,
cnn_extractor=nature_cnn, feature_extraction="cnn", reg_weight=0.0,
layer_norm=False, act_fun=tf.nn.relu, **kwargs):
super(FeedForwardPolicy, self).__init__(sess, ob_space, ac_space, n_env, n_steps, n_batch,
reuse=reuse, scale=(feature_extraction == "cnn"))

self._kwargs_check(feature_extraction, kwargs)
self.layer_norm = layer_norm
self.feature_extraction = feature_extraction
self.cnn_kwargs = kwargs
self.cnn_extractor = cnn_extractor
self.reuse = reuse
if layers is None:
layers = [64, 64]
self.layers = layers
self.reg_loss = None
self.reg_weight = reg_weight
self.entropy = None

assert len(layers) >= 1, "Error: must have at least one hidden layer for the policy."

self.activ_fn = act_fun

def make_actor(self, obs=None, reuse=False, scope="pi"):
if obs is None:
obs = self.processed_obs

with tf.variable_scope(scope, reuse=reuse):
if self.feature_extraction == "cnn":
pi_h = self.cnn_extractor(obs, **self.cnn_kwargs)
else:
pi_h = tf.layers.flatten(obs)

pi_h = mlp(pi_h, self.layers, self.activ_fn, layer_norm=self.layer_norm)

self.act_mu = mu_ = tf.layers.dense(pi_h, self.ac_space.shape[0], activation=None)
# Important difference with SAC and other algo such as PPO:
# the std depends on the state, so we cannot use stable_baselines.common.distribution
log_std = tf.layers.dense(pi_h, self.ac_space.shape[0], activation=None)

# Regularize policy output (not used for now)
# reg_loss = self.reg_weight * 0.5 * tf.reduce_mean(log_std ** 2)
# reg_loss += self.reg_weight * 0.5 * tf.reduce_mean(mu ** 2)
# self.reg_loss = reg_loss

# OpenAI Variation to cap the standard deviation
# activation = tf.tanh # for log_std
# log_std = LOG_STD_MIN + 0.5 * (LOG_STD_MAX - LOG_STD_MIN) * (log_std + 1)
# Original Implementation
log_std = tf.clip_by_value(log_std, LOG_STD_MIN, LOG_STD_MAX)

self.std = std = tf.exp(log_std)
# Reparameterization trick
pi_ = mu_ + tf.random_normal(tf.shape(mu_)) * std
logp_pi = gaussian_likelihood(pi_, mu_, log_std)
self.entropy = gaussian_entropy(log_std)
# MISSING: reg params for log and mu
# Apply squashing and account for it in the probability
deterministic_policy, policy, logp_pi = apply_squashing_func(mu_, pi_, logp_pi)
self.policy = policy
self.deterministic_policy = deterministic_policy

return deterministic_policy, policy, logp_pi

def make_critics(self, obs=None, action=None, reuse=False, scope="values_fn",
create_vf=True, create_qf=True):
if obs is None:
obs = self.processed_obs

with tf.variable_scope(scope, reuse=reuse):
if self.feature_extraction == "cnn":
critics_h = self.cnn_extractor(obs, **self.cnn_kwargs)
else:
critics_h = tf.layers.flatten(obs)

if create_vf:
# Value function
with tf.variable_scope('vf', reuse=reuse):
vf_h = mlp(critics_h, self.layers, self.activ_fn, layer_norm=self.layer_norm)
value_fn = tf.layers.dense(vf_h, 1, name="vf")
self.value_fn = value_fn

if create_qf:
# Concatenate preprocessed state and action
qf_h = tf.concat([critics_h, action], axis=-1)

# Double Q values to reduce overestimation
with tf.variable_scope('qf1', reuse=reuse):
qf1_h = mlp(qf_h, self.layers, self.activ_fn, layer_norm=self.layer_norm)
qf1 = tf.layers.dense(qf1_h, 1, name="qf1")

with tf.variable_scope('qf2', reuse=reuse):
qf2_h = mlp(qf_h, self.layers, self.activ_fn, layer_norm=self.layer_norm)
qf2 = tf.layers.dense(qf2_h, 1, name="qf2")

self.qf1 = qf1
self.qf2 = qf2

return self.qf1, self.qf2, self.value_fn

def step(self, obs, state=None, mask=None, deterministic=False):
if deterministic:
return self.sess.run(self.deterministic_policy, {self.obs_ph: obs})
return self.sess.run(self.policy, {self.obs_ph: obs})

return self.sess.run([self.act_mu, self.std], {self.obs_ph: obs})

[docs]class CnnPolicy(FeedForwardPolicy):
"""
Policy object that implements actor critic, using a CNN (the nature CNN)

:param sess: (TensorFlow session) The current TensorFlow session
:param ob_space: (Gym Space) The observation space of the environment
:param ac_space: (Gym Space) The action space of the environment
:param n_env: (int) The number of environments to run
:param n_steps: (int) The number of steps to run for each environment
:param n_batch: (int) The number of batch to run (n_envs * n_steps)
:param reuse: (bool) If the policy is reusable or not
:param _kwargs: (dict) Extra keyword arguments for the nature CNN feature extraction
"""

def __init__(self, sess, ob_space, ac_space, n_env=1, n_steps=1, n_batch=None, reuse=False, **_kwargs):
super(CnnPolicy, self).__init__(sess, ob_space, ac_space, n_env, n_steps, n_batch, reuse,
feature_extraction="cnn", **_kwargs)

[docs]class LnCnnPolicy(FeedForwardPolicy):
"""
Policy object that implements actor critic, using a CNN (the nature CNN), with layer normalisation

:param sess: (TensorFlow session) The current TensorFlow session
:param ob_space: (Gym Space) The observation space of the environment
:param ac_space: (Gym Space) The action space of the environment
:param n_env: (int) The number of environments to run
:param n_steps: (int) The number of steps to run for each environment
:param n_batch: (int) The number of batch to run (n_envs * n_steps)
:param reuse: (bool) If the policy is reusable or not
:param _kwargs: (dict) Extra keyword arguments for the nature CNN feature extraction
"""

def __init__(self, sess, ob_space, ac_space, n_env=1, n_steps=1, n_batch=None, reuse=False, **_kwargs):
super(LnCnnPolicy, self).__init__(sess, ob_space, ac_space, n_env, n_steps, n_batch, reuse,
feature_extraction="cnn", layer_norm=True, **_kwargs)

[docs]class MlpPolicy(FeedForwardPolicy):
"""
Policy object that implements actor critic, using a MLP (2 layers of 64)

:param sess: (TensorFlow session) The current TensorFlow session
:param ob_space: (Gym Space) The observation space of the environment
:param ac_space: (Gym Space) The action space of the environment
:param n_env: (int) The number of environments to run
:param n_steps: (int) The number of steps to run for each environment
:param n_batch: (int) The number of batch to run (n_envs * n_steps)
:param reuse: (bool) If the policy is reusable or not
:param _kwargs: (dict) Extra keyword arguments for the nature CNN feature extraction
"""

def __init__(self, sess, ob_space, ac_space, n_env=1, n_steps=1, n_batch=None, reuse=False, **_kwargs):
super(MlpPolicy, self).__init__(sess, ob_space, ac_space, n_env, n_steps, n_batch, reuse,
feature_extraction="mlp", **_kwargs)

[docs]class LnMlpPolicy(FeedForwardPolicy):
"""
Policy object that implements actor critic, using a MLP (2 layers of 64), with layer normalisation

:param sess: (TensorFlow session) The current TensorFlow session
:param ob_space: (Gym Space) The observation space of the environment
:param ac_space: (Gym Space) The action space of the environment
:param n_env: (int) The number of environments to run
:param n_steps: (int) The number of steps to run for each environment
:param n_batch: (int) The number of batch to run (n_envs * n_steps)
:param reuse: (bool) If the policy is reusable or not
:param _kwargs: (dict) Extra keyword arguments for the nature CNN feature extraction
"""

def __init__(self, sess, ob_space, ac_space, n_env=1, n_steps=1, n_batch=None, reuse=False, **_kwargs):
super(LnMlpPolicy, self).__init__(sess, ob_space, ac_space, n_env, n_steps, n_batch, reuse,
feature_extraction="mlp", layer_norm=True, **_kwargs)

register_policy("CnnPolicy", CnnPolicy)
register_policy("LnCnnPolicy", LnCnnPolicy)
register_policy("MlpPolicy", MlpPolicy)
register_policy("LnMlpPolicy", LnMlpPolicy)