Custom Policy Network¶
Stable baselines provides default policy networks (see Policies ) for images (CNNPolicies) and other type of input features (MlpPolicies).
One way of customising the policy network architecture is to pass arguments when creating the model,
using policy_kwargs parameter:
import gym
import tensorflow as tf
from stable_baselines import PPO2
# Custom MLP policy of two layers of size 32 each with tanh activation function
policy_kwargs = dict(act_fun=tf.nn.tanh, net_arch=[32, 32])
# Create the agent
model = PPO2("MlpPolicy", "CartPole-v1", policy_kwargs=policy_kwargs, verbose=1)
# Retrieve the environment
env = model.get_env()
# Train the agent
model.learn(total_timesteps=100000)
# Save the agent
model.save("ppo2-cartpole")
del model
# the policy_kwargs are automatically loaded
model = PPO2.load("ppo2-cartpole")
You can also easily define a custom architecture for the policy (or value) network:
Note
Defining a custom policy class is equivalent to passing policy_kwargs.
However, it lets you name the policy and so makes usually the code clearer.
policy_kwargs should be rather used when doing hyperparameter search.
import gym
from stable_baselines.common.policies import FeedForwardPolicy, register_policy
from stable_baselines.common.vec_env import DummyVecEnv
from stable_baselines import A2C
# Custom MLP policy of three layers of size 128 each
class CustomPolicy(FeedForwardPolicy):
def __init__(self, *args, **kwargs):
super(CustomPolicy, self).__init__(*args, **kwargs,
net_arch=[dict(pi=[128, 128, 128],
vf=[128, 128, 128])],
feature_extraction="mlp")
# Create and wrap the environment
env = gym.make('LunarLander-v2')
env = DummyVecEnv([lambda: env])
model = A2C(CustomPolicy, env, verbose=1)
# Train the agent
model.learn(total_timesteps=100000)
# Save the agent
model.save("a2c-lunar")
del model
# When loading a model with a custom policy
# you MUST pass explicitly the policy when loading the saved model
model = A2C.load("a2c-lunar", policy=CustomPolicy)
Warning
When loading a model with a custom policy, you must pass the custom policy explicitly when loading the model. (cf previous example)
You can also register your policy, to help with code simplicity: you can refer to your custom policy using a string.
import gym
from stable_baselines.common.policies import FeedForwardPolicy, register_policy
from stable_baselines.common.vec_env import DummyVecEnv
from stable_baselines import A2C
# Custom MLP policy of three layers of size 128 each
class CustomPolicy(FeedForwardPolicy):
def __init__(self, *args, **kwargs):
super(CustomPolicy, self).__init__(*args, **kwargs,
net_arch=[dict(pi=[128, 128, 128],
vf=[128, 128, 128])],
feature_extraction="mlp")
# Register the policy, it will check that the name is not already taken
register_policy('CustomPolicy', CustomPolicy)
# Because the policy is now registered, you can pass
# a string to the agent constructor instead of passing a class
model = A2C(policy='CustomPolicy', env='LunarLander-v2', verbose=1).learn(total_timesteps=100000)
Deprecated since version 2.3.0: Use net_arch instead of layers parameter to define the network architecture. It allows to have a greater control.
The net_arch parameter of FeedForwardPolicy allows to specify the amount and size of the hidden layers and how many
of them are shared between the policy network and the value network. It is assumed to be a list with the following
structure:
- An arbitrary length (zero allowed) number of integers each specifying the number of units in a shared layer. If the number of ints is zero, there will be no shared layers.
- An optional dict, to specify the following non-shared layers for the value network and the policy network.
It is formatted like
dict(vf=[<value layer sizes>], pi=[<policy layer sizes>]). If it is missing any of the keys (pi or vf), no non-shared layers (empty list) is assumed.
In short: [<shared layers>, dict(vf=[<non-shared value network layers>], pi=[<non-shared policy network layers>])].
Examples¶
Two shared layers of size 128: net_arch=[128, 128]
obs
|
<128>
|
<128>
/ \
action value
Value network deeper than policy network, first layer shared: net_arch=[128, dict(vf=[256, 256])]
obs
|
<128>
/ \
action <256>
|
<256>
|
value
Initially shared then diverging: [128, dict(vf=[256], pi=[16])]
obs
|
<128>
/ \
<16> <256>
| |
action value
The LstmPolicy can be used to construct recurrent policies in a similar way:
class CustomLSTMPolicy(LstmPolicy):
def __init__(self, sess, ob_space, ac_space, n_env, n_steps, n_batch, n_lstm=64, reuse=False, **_kwargs):
super().__init__(sess, ob_space, ac_space, n_env, n_steps, n_batch, n_lstm, reuse,
net_arch=[8, 'lstm', dict(vf=[5, 10], pi=[10])],
layer_norm=True, feature_extraction="mlp", **_kwargs)
Here the net_arch parameter takes an additional (mandatory) ‘lstm’ entry within the shared network section.
The LSTM is shared between value network and policy network.
If your task requires even more granular control over the policy architecture, you can redefine the policy directly:
import gym
import tensorflow as tf
from stable_baselines.common.policies import ActorCriticPolicy, register_policy, nature_cnn
from stable_baselines.common.vec_env import DummyVecEnv
from stable_baselines import A2C
# Custom MLP policy of three layers of size 128 each for the actor and 2 layers of 32 for the critic,
# with a nature_cnn feature extractor
class CustomPolicy(ActorCriticPolicy):
def __init__(self, sess, ob_space, ac_space, n_env, n_steps, n_batch, reuse=False, **kwargs):
super(CustomPolicy, self).__init__(sess, ob_space, ac_space, n_env, n_steps, n_batch, reuse=reuse, scale=True)
with tf.variable_scope("model", reuse=reuse):
activ = tf.nn.relu
extracted_features = nature_cnn(self.processed_obs, **kwargs)
extracted_features = tf.layers.flatten(extracted_features)
pi_h = extracted_features
for i, layer_size in enumerate([128, 128, 128]):
pi_h = activ(tf.layers.dense(pi_h, layer_size, name='pi_fc' + str(i)))
pi_latent = pi_h
vf_h = extracted_features
for i, layer_size in enumerate([32, 32]):
vf_h = activ(tf.layers.dense(vf_h, layer_size, name='vf_fc' + str(i)))
value_fn = tf.layers.dense(vf_h, 1, name='vf')
vf_latent = vf_h
self._proba_distribution, self._policy, self.q_value = \
self.pdtype.proba_distribution_from_latent(pi_latent, vf_latent, init_scale=0.01)
self._value_fn = value_fn
self._setup_init()
def step(self, obs, state=None, mask=None, deterministic=False):
if deterministic:
action, value, neglogp = self.sess.run([self.deterministic_action, self.value_flat, self.neglogp],
{self.obs_ph: obs})
else:
action, value, neglogp = self.sess.run([self.action, self.value_flat, self.neglogp],
{self.obs_ph: obs})
return action, value, self.initial_state, neglogp
def proba_step(self, obs, state=None, mask=None):
return self.sess.run(self.policy_proba, {self.obs_ph: obs})
def value(self, obs, state=None, mask=None):
return self.sess.run(self.value_flat, {self.obs_ph: obs})
# Create and wrap the environment
env = DummyVecEnv([lambda: gym.make('Breakout-v0')])
model = A2C(CustomPolicy, env, verbose=1)
# Train the agent
model.learn(total_timesteps=100000)