* Quack-TD

Quack-TD is a backgammon playing algorithm based upon neural networks trained
through TD(\lambda)-learning.

** Usage

The main executable is =main.py=. Various command-line options and switches can be used to
execute different stages and modify the behaviour of the program. All
command-line options and switches are listed by running =main.py= with the argument
=--help=. The three central switches are listed below:

- =--train=: Trains the neural network for a set amount of episodes (full games
  of backgammon) set by =--episodes= (defaults to 1,000).

- =--eval=: Evaluates the nerual network using the methods specified by
  =--eval-methods= for a the amount of episodes set by =--episodes= (defaults to
  1,000).

- =--play=: Allows the user to interactively play a game of backgammon against
  the algorithm.

** Model storage format

Models are stored in the directory =models=. If no model is specfied with the
=--model= option, the model is stored in the =models/default=
directory. Otherwise, the model is stored in =models/$MODEL=.

*** Files

Along with the Tensorflow checkpoint files in the directory, the following files
are stored:

- =model.episodes=: The number of episodes of training performed with the
  model
- =logs/eval.log=: Log of all completed evaluations performed on the model. The
  format of this file is specified in [[Log format]].
- =logs/train.log=: Log of all completed training sessions performed on the
  model. If a training session is aborted before the pre-specified episode
  target is reached, nothing will be written to this file, although
  =model.episodes= will be updated every time the model is saved to disk. The
  format of this file is specified in [[Log format]].

*** Log format

The evaluation and training log files (=logs/eval.log= and =logs/train.log=
respectively) are CSV-foramtted files with structure as described below. Both
files have semicolon-separated columns (=;=) and newline-separated rows (=\n=).

**** Evaluation log (=eval.log=)

Columns are written in the following order:

- =time=: Unix time (Epoch time) timestamp in local time (TODO: should be UTC
  instead?) describing when the evaluation was finished.
- =method=: Short string describing the method used for evaluation.
- =trained_eps=: Amount of episodes trained with the model before evaluation
- =count=: Amount of episodes used for evaluation
- =sum=: Sum of outcomes of the games played during evaluation. Outcomes are
  integers in the range of -2 to 2. A sum of 0 indicates that the evaluated
  algorithm scored neutrally. (TODO: Is this true?)
- =mean=: Mean of outcomes of the games played during evaluation. Outcomes are
  integers in the range of -2 to 2. A mean of 0 indicates that the evaluated
  algorithm scored neutrally. (TODO: Is this true?)

TODO: Add example of log row

**** Training log (=train.log=)

Columns are written in the following order:

- =time=: Unix time (Epoch time) timestamp in local time (TODO: should be UTC
  instead?) describing when the training session was finished.
- =trained_eps=: Amount of episodes trained with the model /after/ the training
  session
- =count=: Amount of episodes used for training
- =sum=: Sum of outcomes of the games played during training. Outcomes are
  integers in the range of -2 to 2. A sum of 0 indicates that the evaluated
  algorithm scored neutrally. (TODO: Is this true?)
- =mean=: Mean of outcomes of the games played during training. Outcomes are
  integers in the range of -2 to 2. A mean of 0 indicates that the evaluated
  algorithm scored neutrally. (TODO: Is this true?)