[rllib] Rename algorithms (#890)

* rename algorithms

* fix

* fix jenkins test

* fix documentation

* fix

python/ray/rllib/ppo/env.py

@@ -0,0 +1,43 @@
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import gym
+import numpy as np
+
+from ray.rllib.models import ModelCatalog
+
+
+class BatchedEnv(object):
+    """This holds multiple gym envs and performs steps on all of them."""
+    def __init__(self, name, batchsize):
+        self.envs = [gym.make(name) for _ in range(batchsize)]
+        self.observation_space = self.envs[0].observation_space
+        self.action_space = self.envs[0].action_space
+        self.batchsize = batchsize
+        self.preprocessor = ModelCatalog.get_preprocessor(
+            name, self.envs[0].observation_space.shape)
+
+    def reset(self):
+        observations = [
+            self.preprocessor.transform(env.reset()) for env in self.envs]
+        self.shape = observations[0].shape
+        self.dones = [False for _ in range(self.batchsize)]
+        return np.vstack(observations)
+
+    def step(self, actions, render=False):
+        observations = []
+        rewards = []
+        for i, action in enumerate(actions):
+            if self.dones[i]:
+                observations.append(np.zeros(self.shape))
+                rewards.append(0.0)
+                continue
+            observation, reward, done, info = self.envs[i].step(action)
+            if render:
+                self.envs[0].render()
+            observations.append(self.preprocessor.transform(observation))
+            rewards.append(reward)
+            self.dones[i] = done
+        return (np.vstack(observations), np.array(rewards, dtype="float32"),
+                np.array(self.dones))