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vendor/github.com/ecooper/qlearning/README.md

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+# qlearning
+
+The qlearning package provides a series of interfaces and utilities to implement
+the [Q-Learning](https://en.wikipedia.org/wiki/Q-learning) algorithm in
+Go.
+
+This project was largely inspired by [flappybird-qlearning-
+bot](https://github.com/chncyhn/flappybird-qlearning-bot).
+
+*Until a release is tagged, qlearning should be considered highly
+experimental and mostly a fun toy.*
+
+## Installation
+
+```shell
+$ go get https://github.com/ecooper/qlearning
+```
+
+## Quickstart
+
+qlearning provides example implementations in the [examples](examples/)
+directory of the project.
+
+[hangman.go](examples/hangman.go) provides a naive implementation of
+[Hangman](https://en.wikipedia.org/wiki/Hangman_(game)) for use with
+qlearning.
+
+```shell
+$ cd $GOPATH/src/github.com/ecooper/qlearning/examples
+$ go run hangman.go -h
+Usage of hangman:
+  -debug
+        Set debug
+  -games int
+        Play N games (default 5000000)
+  -progress int
+        Print progress messages every N games (default 1000)
+  -wordlist string
+        Path to a wordlist (default "./wordlist.txt")
+  -words int
+        Use N words from wordlist (default 10000)
+```
+
+By default, running [hangman.go](examples/hangman.go) will play millions
+of games against a 10,000-word corpus. That's a bit overkill for just
+trying out qlearning. You can run it against a smaller number of words
+for a few number of games using the `-games` and `-words` flags.
+
+```shell
+$ go run hangman.go -words 100 -progress 1000 -games 5000
+100 words loaded
+1000 games played: 92 WINS 908 LOSSES 9% WIN RATE
+2000 games played: 447 WINS 1553 LOSSES 36% WIN RATE
+3000 games played: 1064 WINS 1936 LOSSES 62% WIN RATE
+4000 games played: 1913 WINS 2087 LOSSES 85% WIN RATE
+5000 games played: 2845 WINS 2155 LOSSES 93% WIN RATE
+
+Agent performance: 5000 games played, 2845 WINS 2155 LOSSES 57% WIN RATE
+```
+
+"WIN RATE" per progress report is isolated within that cycle, a group of
+1000 games in this example. The win rate is meant to show the velocity
+of learning by the agent. If it is "learning", the win rate should be
+increasing until reaching convergence.
+
+As you can see, after 5000 games, the agent is able to "learn" and play
+hangman against a 100-word vocabulary.
+
+## Usage
+
+See [godocs](https://godoc.org/github.com/ecooper/qlearning) for the
+package documentation.