.. deepair documentation master file, created by
   sphinx-quickstart on Tue May 31 02:38:44 2022.
   You can adapt this file completely to your liking, but it should at least
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Welcome to Deepair's documentation!
===================================

**Deepair** (/diːp er/) is a Python library that have reliable implementations of Deep reinforcement learning algorithms.

Simple way to start
-------------------
Install deepair with pip

.. code-block:: bash

   pip install deepair

start with gym env

.. code-block:: python

   import gym
   from deepair.dqn import Rainbow

   env = gym.make('LunarLander-v2')

   rain = Rainbow(env=env, memory_size=10000, batch_size=32, target_update=256)

   rain.train(timesteps=200000)

   # test
   state = env.reset()
   done = False
   score = 0

   while not done:
      action = rain.select_action(state, deterministic=True)
      next_state, reward, done, info = env.step(action)

      state = next_state
      score += reward

   print("score: ", score)

.. image:: _static/img/rainbow_lunalander.gif
   :width: 600


.. toctree::
   :maxdepth: 1

   install
   tutorial
   rlalgorithms
   custom_technique
   developer_guides
   modules





Indices and tables
==================

* :ref:`genindex`
* :ref:`modindex`
* :ref:`search`