Initial commit

docs/index.rst

@@ -0,0 +1,69 @@
+retro-gamer: train agents to play retro games
+==============================================
+
+``retro-gamer`` is a Python package for training reinforcement learning
+agents to play games implemented with the
+`retro-games <https://retro-games.readthedocs.io/en/latest/>`__
+framework. It is designed as a learning tool: rather than writing the
+learning algorithm yourself, you describe the game to the trainer in a
+structured way, adjust the training parameters, and then observe—through
+a detailed log—how the trainer uses your description to build and run a
+learning model.
+
+The central idea is that the game becomes an *object to think with*
+about reinforcement learning. The choices you make—which characters to
+tell the trainer about, what counts as a reward, whether to treat the
+board as a spatial scene or a readout—have direct, observable
+consequences for how learning proceeds. Working out *why* a training run
+behaves as it does is the kind of reasoning that leads to lasting
+understanding of the underlying concepts.
+
+.. _installation:
+
+Installation
+------------
+
+Prerequisites
+~~~~~~~~~~~~~
+
+``retro-gamer`` requires Python 3.11 or higher and a game implemented
+with `retro-games <https://retro-games.readthedocs.io/en/latest/>`__.
+The retro-games framework must also be installed; see its documentation
+for instructions.
+
+.. code-block:: console
+
+   % pip install retro-gamer
+
+To install from source (for development or to use the latest changes):
+
+.. code-block:: console
+
+   % git clone https://github.com/cproctor/retro-gamer
+   % cd retro-gamer
+   % pip install -e .
+
+Verify the installation by checking the command-line tool:
+
+.. code-block:: console
+
+   % retro-gamer --help
+   Usage: retro-gamer [OPTIONS] COMMAND [ARGS]...
+
+     Train and run RL agents for retro games.
+
+   Commands:
+     create  Create a new training run directory with config.toml.
+     info    Print a summary of a training run.
+     play    Watch a trained agent play the game.
+     train   Train (or resume training) a DQN agent.
+
+.. toctree::
+   :maxdepth: 1
+   :caption: Contents:
+
+   introduction
+   background
+   walkthrough
+   reference
+   contributing