README.rst

CS221-Autum2018
Jenna Lee (yunjlee)
Jennifer Villa (jvilla3)

====================================
Source codes for all attempted Q-learning and function approximation solution to Lunarlander-v2. 

0. Before running any source codes, set up the conda environment using
conda env create -f environment.yml

Below are the commands/source files that can replicate differnet solutions:

---python keyboard_agent.py
: Available from OpenAIGym. This is a good way to gain intuition about the game. You may play the game of attempting to softly land on the terrain by choosing one of the four actions (Do nothing - numpad 0; fire main engine - numpad 2; fire left engine - numpad 1; fire right engine -3). 

---python qlearning.py 
This source code will solve the simple lunarlander problem in 1D setup. Most of system noise had been lifted, and the only action that lunar lander can take is either do nothing, or firing the main engine. 

---python qlearning_feat_1d_full.py
This source code will solve the simple lunarlander problem with full frame. This required application of the same action for a certain amount of steps (intended delayed calculation of next steps)

---python qlearning_feat.py 
This source code will solve the full 2d lunarlander problem with bigger feature extractor. Unfortunately, now the problem has become too complicated that our solution fails to converge on the full problem as mentioned in the final report. 

---python qlearning_deep.py
This source code will attempt to solve the full 2d lunarlander problem with neural network to extract features instead of linear approximation of feature weights. 


==========================================================================================





OpenAI Gym
**********

**OpenAI Gym is a toolkit for developing and comparing reinforcement learning algorithms.** This is the ``gym`` open-source library, which gives you access to a standardized set of environments.

.. image:: https://travis-ci.org/openai/gym.svg?branch=master
    :target: https://travis-ci.org/openai/gym

`See What's New section below <#what-s-new>`_

``gym`` makes no assumptions about the structure of your agent, and is compatible with any numerical computation library, such as TensorFlow or Theano. You can use it from Python code, and soon from other languages.

If you're not sure where to start, we recommend beginning with the
`docs <https://gym.openai.com/docs>`_ on our site. See also the `FAQ <https://github.com/openai/gym/wiki/FAQ>`_.

A whitepaper for OpenAI Gym is available at http://arxiv.org/abs/1606.01540, and here's a BibTeX entry that you can use to cite it in a publication::

  @misc{1606.01540,
    Author = {Greg Brockman and Vicki Cheung and Ludwig Pettersson and Jonas Schneider and John Schulman and Jie Tang and Wojciech Zaremba},
    Title = {OpenAI Gym},
    Year = {2016},
    Eprint = {arXiv:1606.01540},
  }

.. contents:: **Contents of this document**
   :depth: 2

Basics
======

There are two basic concepts in reinforcement learning: the
environment (namely, the outside world) and the agent (namely, the
algorithm you are writing). The agent sends `actions` to the
environment, and the environment replies with `observations` and
`rewards` (that is, a score).

The core `gym` interface is `Env <https://github.com/openai/gym/blob/master/gym/core.py>`_, which is
the unified environment interface. There is no interface for agents;
that part is left to you. The following are the ``Env`` methods you
should know:

- `reset(self)`: Reset the environment's state. Returns `observation`.
- `step(self, action)`: Step the environment by one timestep. Returns `observation`, `reward`, `done`, `info`.
- `render(self, mode='human', close=False)`: Render one frame of the environment. The default mode will do something human friendly, such as pop up a window. Passing the `close` flag signals the renderer to close any such windows.

Installation
============

You can perform a minimal install of ``gym`` with:

.. code:: shell

    git clone https://github.com/openai/gym.git
    cd gym
    pip install -e .

If you prefer, you can do a minimal install of the packaged version directly from PyPI:

.. code:: shell

    pip install gym

You'll be able to run a few environments right away:

- algorithmic
- toy_text
- classic_control (you'll need ``pyglet`` to render though)

We recommend playing with those environments at first, and then later
installing the dependencies for the remaining environments.

Installing everything
---------------------

To install the full set of environments, you'll need to have some system
packages installed. We'll build out the list here over time; please let us know
what you end up installing on your platform. Also, take a look at the docker files (test.dockerfile.xx.xx) to 
see the composition of our CI-tested images. 

On OSX:

.. code:: shell

    brew install cmake boost boost-python sdl2 swig wget

On Ubuntu 14.04 (non-mujoco only):

.. code:: shell

    apt-get install libjpeg-dev cmake swig python-pyglet python3-opengl libboost-all-dev \
            libsdl2-2.0.0 libsdl2-dev libglu1-mesa libglu1-mesa-dev libgles2-mesa-dev \
            freeglut3 xvfb libav-tools


On Ubuntu 16.04:

.. code:: shell

    apt-get install -y python-pyglet python3-opengl zlib1g-dev libjpeg-dev patchelf \
            cmake swig libboost-all-dev libsdl2-dev libosmesa6-dev xvfb ffmpeg

On Ubuntu 18.04:

.. code:: shell

    apt install -y python3-dev zlib1g-dev libjpeg-dev cmake swig python-pyglet python3-opengl libboost-all-dev libsdl2-dev \
        libosmesa6-dev patchelf ffmpeg xvfb


MuJoCo has a proprietary dependency we can't set up for you. Follow
the
`instructions <https://github.com/openai/mujoco-py#obtaining-the-binaries-and-license-key>`_
in the ``mujoco-py`` package for help.

Once you're ready to install everything, run ``pip install -e '.[all]'`` (or ``pip install 'gym[all]'``).

Supported systems
-----------------

We currently support Linux and OS X running Python 2.7 or 3.5. Some users on OSX + Python3 may need to run

.. code:: shell

    brew install boost-python --with-python3

If you want to access Gym from languages other than python, we have limited support for non-python
frameworks, such as lua/Torch, using the OpenAI Gym `HTTP API <https://github.com/openai/gym-http-api>`_.

Pip version
-----------

To run ``pip install -e '.[all]'``, you'll need a semi-recent pip.
Please make sure your pip is at least at version ``1.5.0``. You can
upgrade using the following: ``pip install --ignore-installed
pip``. Alternatively, you can open `setup.py
<https://github.com/openai/gym/blob/master/setup.py>`_ and
install the dependencies by hand.

Rendering on a server
---------------------

If you're trying to render video on a server, you'll need to connect a
fake display. The easiest way to do this is by running under
``xvfb-run`` (on Ubuntu, install the ``xvfb`` package):

.. code:: shell

     xvfb-run -s "-screen 0 1400x900x24" bash

Installing dependencies for specific environments
-------------------------------------------------

If you'd like to install the dependencies for only specific
environments, see `setup.py
<https://github.com/openai/gym/blob/master/setup.py>`_. We
maintain the lists of dependencies on a per-environment group basis.

Environments
============

The code for each environment group is housed in its own subdirectory
`gym/envs
<https://github.com/openai/gym/blob/master/gym/envs>`_. The
specification of each task is in `gym/envs/__init__.py
<https://github.com/openai/gym/blob/master/gym/envs/__init__.py>`_. It's
worth browsing through both.

Algorithmic
-----------

These are a variety of algorithmic tasks, such as learning to copy a
sequence.

.. code:: python

    import gym
    env = gym.make('Copy-v0')
    env.reset()
    env.render()

Atari
-----

The Atari environments are a variety of Atari video games. If you didn't do the full install, you can install dependencies via ``pip install -e '.[atari]'`` (you'll need ``cmake`` installed) and then get started as follow:

.. code:: python

    import gym
    env = gym.make('SpaceInvaders-v0')
    env.reset()
    env.render()

This will install ``atari-py``, which automatically compiles the `Arcade Learning Environment <http://www.arcadelearningenvironment.org/>`_. This can take quite a while (a few minutes on a decent laptop), so just be prepared.

Box2d
-----------

Box2d is a 2D physics engine. You can install it via  ``pip install -e '.[box2d]'`` and then get started as follow:

.. code:: python

    import gym
    env = gym.make('LunarLander-v2')
    env.reset()
    env.render()

Classic control
---------------

These are a variety of classic control tasks, which would appear in a typical reinforcement learning textbook. If you didn't do the full install, you will need to run ``pip install -e '.[classic_control]'`` to enable rendering. You can get started with them via:

.. code:: python

    import gym
    env = gym.make('CartPole-v0')
    env.reset()
    env.render()

MuJoCo
------

`MuJoCo <http://www.mujoco.org/>`_ is a physics engine which can do
very detailed efficient simulations with contacts. It's not
open-source, so you'll have to follow the instructions in `mujoco-py
<https://github.com/openai/mujoco-py#obtaining-the-binaries-and-license-key>`_
to set it up. You'll have to also run ``pip install -e '.[mujoco]'`` if you didn't do the full install.

.. code:: python

    import gym
    env = gym.make('Humanoid-v2')
    env.reset()
    env.render()


Robotics
------

`MuJoCo <http://www.mujoco.org/>`_ is a physics engine which can do
very detailed efficient simulations with contacts and we use it for all robotics environments. It's not
open-source, so you'll have to follow the instructions in `mujoco-py
<https://github.com/openai/mujoco-py#obtaining-the-binaries-and-license-key>`_
to set it up. You'll have to also run ``pip install -e '.[robotics]'`` if you didn't do the full install.

.. code:: python

    import gym
    env = gym.make('HandManipulateBlock-v0')
    env.reset()
    env.render()

You can also find additional details in the accompanying `technical report <https://arxiv.org/abs/1802.09464>`_ and `blog post <https://blog.openai.com/ingredients-for-robotics-research/>`_.
If you use these environments, you can cite them as follows::

  @misc{1802.09464,
    Author = {Matthias Plappert and Marcin Andrychowicz and Alex Ray and Bob McGrew and Bowen Baker and Glenn Powell and Jonas Schneider and Josh Tobin and Maciek Chociej and Peter Welinder and Vikash Kumar and Wojciech Zaremba},
    Title = {Multi-Goal Reinforcement Learning: Challenging Robotics Environments and Request for Research},
    Year = {2018},
    Eprint = {arXiv:1802.09464},
  }

Toy text
--------

Toy environments which are text-based. There's no extra dependency to install, so to get started, you can just do:

.. code:: python

    import gym
    env = gym.make('FrozenLake-v0')
    env.reset()
    env.render()

Examples
========

See the ``examples`` directory.

- Run `examples/agents/random_agent.py <https://github.com/openai/gym/blob/master/examples/agents/random_agent.py>`_ to run an simple random agent.
- Run `examples/agents/cem.py <https://github.com/openai/gym/blob/master/examples/agents/cem.py>`_ to run an actual learning agent (using the cross-entropy method).
- Run `examples/scripts/list_envs <https://github.com/openai/gym/blob/master/examples/scripts/list_envs>`_ to generate a list of all environments.

Testing
=======

We are using `pytest <http://doc.pytest.org>`_ for tests. You can run them via:

.. code:: shell

    pytest


.. _See What's New section below:

What's new
==========

- 2018-02-28: Release of a set of new robotics environments.
- 2018-01-25: Made some aesthetic improvements and removed unmaintained parts of gym. This may seem like a downgrade in functionality, but it is actually a long-needed cleanup in preparation for some great new things that will be released in the next month.

    + Now your `Env` and `Wrapper` subclasses should define `step`, `reset`, `render`, `close`, `seed` rather than underscored method names.
    + Removed the `board_game`, `debugging`, `safety`, `parameter_tuning` environments since they're not being maintained by us at OpenAI. We encourage authors and users to create new repositories for these environments.
    + Changed `MultiDiscrete` action space to range from `[0, ..., n-1]` rather than `[a, ..., b-1]`.
    + No more `render(close=True)`, use env-specific methods to close the rendering.
    + Removed `scoreboard` directory, since site doesn't exist anymore.
    + Moved `gym/monitoring` to `gym/wrappers/monitoring`
    + Add `dtype` to `Space`.
    + Not using python's built-in module anymore, using `gym.logger`

- 2018-01-24: All continuous control environments now use mujoco_py >= 1.50.
  Versions have been updated accordingly to -v2, e.g. HalfCheetah-v2. Performance
  should be similar (see https://github.com/openai/gym/pull/834) but there are likely
  some differences due to changes in MuJoCo.
- 2017-06-16: Make env.spec into a property to fix a bug that occurs
  when you try to print out an unregistered Env.
- 2017-05-13: BACKWARDS INCOMPATIBILITY: The Atari environments are now at
  *v4*. To keep using the old v3 environments, keep gym <= 0.8.2 and atari-py
  <= 0.0.21. Note that the v4 environments will not give identical results to
  existing v3 results, although differences are minor. The v4 environments
  incorporate the latest Arcade Learning Environment (ALE), including several
  ROM fixes, and now handle loading and saving of the emulator state. While
  seeds still ensure determinism, the effect of any given seed is not preserved
  across this upgrade because the random number generator in ALE has changed.
  The `*NoFrameSkip-v4` environments should be considered the canonical Atari
  environments from now on.
- 2017-03-05: BACKWARDS INCOMPATIBILITY: The `configure` method has been removed
  from `Env`. `configure` was not used by `gym`, but was used by some dependent
  libraries including `universe`. These libraries will migrate away from the
  configure method by using wrappers instead. This change is on master and will be released with 0.8.0.
- 2016-12-27: BACKWARDS INCOMPATIBILITY: The gym monitor is now a
  wrapper. Rather than starting monitoring as
  `env.monitor.start(directory)`, envs are now wrapped as follows:
  `env = wrappers.Monitor(env, directory)`. This change is on master
  and will be released with 0.7.0.
- 2016-11-1: Several experimental changes to how a running monitor interacts
  with environments. The monitor will now raise an error if reset() is called
  when the env has not returned done=True. The monitor will only record complete
  episodes where done=True. Finally, the monitor no longer calls seed() on the
  underlying env, nor does it record or upload seed information.
- 2016-10-31: We're experimentally expanding the environment ID format
  to include an optional username.
- 2016-09-21: Switch the Gym automated logger setup to configure the
  root logger rather than just the 'gym' logger.
- 2016-08-17: Calling `close` on an env will also close the monitor
  and any rendering windows.
- 2016-08-17: The monitor will no longer write manifest files in
  real-time, unless `write_upon_reset=True` is passed.
- 2016-05-28: For controlled reproducibility, envs now support seeding
  (cf #91 and #135). The monitor records which seeds are used. We will
  soon add seed information to the display on the scoreboard.