add plots to getting started guide

docs/source/guide/getting_started.rst

@@ -5,17 +5,26 @@ Prerequisites
 -------------
 
 The Autonomous Learning Library requires a recent version of Pytorch (>= 10.3).
-Additionally, Tensorboard is required in order to enable logging,
-which requires the installation of Tensorflow.
-We recommond installing these through `link Conda <https://docs.conda.io/en/latest/>`.
-
-We have also tested on Unix-based machines (Linux and MacOS).
-However, there is no inherent reason why it shouldn 
+Additionally, Tensorboard is required in order to enable logging.
+We recommond installing these through `Conda <https://docs.conda.io/en/latest/>`_.
+We also strongly recommend using a machine with a fast GPU (a GTX 970 or better).
 
 Installation
 ------------
 
-Right now, the Autonomous Learning Library can only be installed by cloning the Github repository::
+The ``autonomous-learning-library`` can be installed from PyPi using ``pip``:
+
+.. code-block:: bash
+
+    pip install autonomous-learning-library
+
+If you're lazy, you can install the prerequisites as well using:
+
+.. code-block:: bash
+
+    pip install autonomous-learning-library[pytorch]
+
+An alternate approach, that may be useful when following this tutorial, is to instead install by cloning the Github repository:
 
     git clone https://github.com/cpnota/autonomous-learning-library.git
     cd autonomous-learning-library
@@ -25,9 +34,7 @@ You can test your installation by running the tests::
 
     make test
 
-If you do not have Make installed, the tests can by run with::
-
-    python -m unittest discover -s all -p "*test.py"
+This should also tell you if CUDA (the GPU driver) is unavailable.
 
 Running a Preset Agent
 ----------------------
@@ -37,20 +44,32 @@ However, the library includes a number of "preset" agent configurations for easy
 It also includes some useful scripts for running the presets.
 For example, an A2C agent can be run on Cart-Pole as follows::
 
-    python scripts/classic.py CartPole-v0 a2c
+    python scripts/classic.py CartPole-v0 ppo
 
-The results will be written to `runs/_a2c <id>`, where `<id>` is some some string generated by the library.
-You can view these results and other information through `tensorboard`::
+The results will be written to ``runs/_a2c <id>``, where ``<id>`` is some some string generated by the library.
+You can view these results and other information through `tensorboard`:
 
     tensorboard --logdir runs
 
+By opening your browser to <http://localhost:6006>, you should see a dashboard that looks something like the following (you may need to adjust the "smoothing" parameter):
+
+.. image:: tensorboard.png
+
 If you want to compare agents in a nicer, format, you can use the `plot` script::
 
     python scripts/plot.py runs
 
+This should give you a plot similar to the following:
+
+.. image:: plot.png
+
+In this plot, each point represents the average of the episodic returns over the last 100 episodes.
+The shaded region represents the standard deviation over that interval.
+
 Finally, to watch the trained model in action, we provide a `watch` scripts for each preset module::
 
     python scripts/watch_classic.py CartPole-v0 "runs/_a2c <id>"
 
+You need to find the <id> by checking the ``runs`` directory.
+
 Be sure to check out the `atari` and `continuous` presets for more fun!
-You will need CUDA and a decent GPU in order to achieve good performance.