[rllib] Initial RLLib documentation (#969)

* initial documentation for RLLib

* more RL documentation

* fix linting

* fix comments

* update

* fix

python/ray/rllib/models/__init__.py

@@ -1,3 +1,12 @@
 from ray.rllib.models.catalog import ModelCatalog
+from ray.rllib.models.action_dist import (ActionDistribution, Categorical,
+                                          DiagGaussian, Deterministic)
+from ray.rllib.models.model import Model
+from ray.rllib.models.fcnet import FullyConnectedNetwork
+from ray.rllib.models.convnet import ConvolutionalNetwork
+from ray.rllib.models.lstm import LSTM
 
-__all__ = ["ModelCatalog"]
+
+__all__ = ["ActionDistribution", "ActionDistribution", "Categorical",
+           "DiagGaussian", "Deterministic", "ModelCatalog", "Model",
+           "FullyConnectedNetwork", "ConvolutionalNetwork", "LSTM"]

python/ray/rllib/models/action_dist.py

@@ -17,15 +17,19 @@ class ActionDistribution(object):
         self.inputs = inputs
 
     def logp(self, x):
+        """The log-likelihood of the action distribution."""
         raise NotImplementedError
 
     def kl(self, other):
+        """The KL-divergene between two action distributions."""
         raise NotImplementedError
 
     def entropy(self):
+        """The entroy of the action distribution."""
         raise NotImplementedError
 
     def sample(self):
+        """Draw a sample from the action distribution."""
         raise NotImplementedError
 
 

python/ray/rllib/models/lstm.py

@@ -11,13 +11,13 @@ from ray.rllib.models.misc import (conv2d, linear, flatten,
                                    normc_initializer)
 from ray.rllib.models.model import Model
 
-use_tf100_api = (distutils.version.LooseVersion(tf.VERSION) >=
-                 distutils.version.LooseVersion("1.0.0"))
-
 
 class LSTM(Model):
     # TODO(rliaw): Add LSTM code for other algorithms
     def _init(self, inputs, num_outputs, options):
+        use_tf100_api = (distutils.version.LooseVersion(tf.VERSION) >=
+                         distutils.version.LooseVersion("1.0.0"))
+
         self.x = x = inputs
         for i in range(4):
             x = tf.nn.elu(conv2d(x, 32, "l{}".format(i + 1), [3, 3], [2, 2]))