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Revision 84ea7aa1fd83966d6a121e60e14dfaac4675c767 authored by AurelianTactics on 24 October 2018, 16:59:46 UTC, committed by pzhokhov on 24 October 2018, 16:59:46 UTC 
DDPG has unused 'seed' argument (#676)
 
DeepQ, PPO2, ACER, trpo_mpi, A2C, and ACKTR have the code for:

```
from baselines.common import set_global_seeds
...
def learn(...):
...
   set_global_seeds(seed)
```

DDPG has the argument 'seed=None' but doesn't have the two lines of code needed to set the global seeds.
1 parent 88300ed
Raw File
models.py 
import tensorflow as tf
from baselines.common.models import get_network_builder


class Model(object):
    def __init__(self, name, network='mlp', **network_kwargs):
        self.name = name
        self.network_builder = get_network_builder(network)(**network_kwargs)

    @property
    def vars(self):
        return tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES, scope=self.name)

    @property
    def trainable_vars(self):
        return tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, scope=self.name)

    @property
    def perturbable_vars(self):
        return [var for var in self.trainable_vars if 'LayerNorm' not in var.name]


class Actor(Model):
    def __init__(self, nb_actions, name='actor', network='mlp', **network_kwargs):
        super().__init__(name=name, network=network, **network_kwargs)
        self.nb_actions = nb_actions

    def __call__(self, obs, reuse=False):
        with tf.variable_scope(self.name, reuse=tf.AUTO_REUSE):
            x = self.network_builder(obs)
            x = tf.layers.dense(x, self.nb_actions, kernel_initializer=tf.random_uniform_initializer(minval=-3e-3, maxval=3e-3))
            x = tf.nn.tanh(x)
        return x


class Critic(Model):
    def __init__(self, name='critic', network='mlp', **network_kwargs):
        super().__init__(name=name, network=network, **network_kwargs)
        self.layer_norm = True

    def __call__(self, obs, action, reuse=False):
        with tf.variable_scope(self.name, reuse=tf.AUTO_REUSE):
            x = tf.concat([obs, action], axis=-1) # this assumes observation and action can be concatenated
            x = self.network_builder(x)
            x = tf.layers.dense(x, 1, kernel_initializer=tf.random_uniform_initializer(minval=-3e-3, maxval=3e-3))
        return x

    @property
    def output_vars(self):
        output_vars = [var for var in self.trainable_vars if 'output' in var.name]
        return output_vars
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