add env.step() (#3068)

Author: Chris Elion

notebooks/getting-started.ipynb

@@ -76,6 +76,8 @@
    "outputs": [],
    "source": [
     "env = UnityEnvironment(file_name=env_name)\n",
+    "# Step once to make sure brains are sent over.\n",
+    "env.step()\n",
     "\n",
     "# Set the default brain to work with\n",
     "default_brain = env.external_brain_names[0]\n",
@@ -116,7 +118,7 @@
    "metadata": {},
    "source": [
     "### 5. Take random actions in the environment\n",
-    "Once we restart an environment, we can step the environment forward and provide actions to all of the agents within the environment. Here we simply choose random actions based on the `action_space_type` of the default brain. \n",
+    "Once we restart an environment, we can step the environment forward and provide actions to all of the agents within the environment. Here we simply choose random actions based on the `action_space_type` of the default brain.\n",
     "\n",
     "Once this cell is executed, 10 messages will be printed that detail how much reward will be accumulated for the next 10 episodes. The Unity environment will then pause, waiting for further signals telling it what to do next. Thus, not seeing any animation is expected when running this cell."
    ]
@@ -134,7 +136,7 @@
     "    while not done:\n",
     "        action_size = brain.vector_action_space_size\n",
     "        if brain.vector_action_space_type == 'continuous':\n",
-    "            env_info = env.step(np.random.randn(len(env_info.agents), \n",
+    "            env_info = env.step(np.random.randn(len(env_info.agents),\n",
     "                                                action_size[0]))[default_brain]\n",
     "        else:\n",
     "            action = np.column_stack([np.random.randint(0, action_size[i], size=(len(env_info.agents))) for i in range(len(action_size))])\n",
@@ -180,7 +182,7 @@
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
    "version": "3.6.6"
-  }
+  },
  },
  "nbformat": 4,
  "nbformat_minor": 1