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https://github.com/wassname/ray.git
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Philipp Moritz
df65e87fc7
* tune ppo on control tasks * introduce free log_std * fix * flag for writing logs * fixes * fixes
61 lines
2.3 KiB
Python
61 lines
2.3 KiB
Python
from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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import tensorflow as tf
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import tensorflow.contrib.slim as slim
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import numpy as np
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from ray.rllib.models.model import Model
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def normc_initializer(std=1.0):
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def _initializer(shape, dtype=None, partition_info=None):
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out = np.random.randn(*shape).astype(np.float32)
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out *= std / np.sqrt(np.square(out).sum(axis=0, keepdims=True))
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return tf.constant(out)
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return _initializer
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class FullyConnectedNetwork(Model):
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"""Generic fully connected network.
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Options to construct the network are passed to the _init function.
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If options["free_logstd"] is True, the last half of the
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output layer will be free variables that are not dependent on
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inputs. This is often used if the output of the network is used
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to parametrize a probability distribution. In this case, the
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first half of the parameters can be interpreted as a location
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parameter (like a mean) and the second half can be interpreted as
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a scale parameter (like a standard deviation).
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"""
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def _init(self, inputs, num_outputs, options):
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hiddens = options.get("fcnet_hiddens", [256, 256])
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activation = options.get("fcnet_activation", tf.nn.tanh)
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print("Constructing fcnet {} {}".format(hiddens, activation))
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if options.get("free_logstd", False):
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num_outputs = num_outputs // 2
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with tf.name_scope("fc_net"):
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i = 1
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last_layer = inputs
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for size in hiddens:
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last_layer = slim.fully_connected(
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last_layer, size,
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weights_initializer=normc_initializer(1.0),
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activation_fn=activation,
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scope="fc{}".format(i))
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i += 1
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output = slim.fully_connected(
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last_layer, num_outputs,
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weights_initializer=normc_initializer(0.01),
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activation_fn=None, scope="fc_out")
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if options.get("free_logstd", False):
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logstd = tf.get_variable(name="logstd", shape=[num_outputs],
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initializer=tf.zeros_initializer)
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output = tf.concat([output, 0.0 * output + logstd], 1)
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return output, last_layer
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