random-stuff/tacnettest.lua

--[[
TacNet is an advanced tic-tac-toe AI player based on a cutting-edge end-to-end RNN (recurrent neural network) approach.

This version was developed at the [Kaggle](https://www.kaggle.com/c/tictactoe) competition, and was part of the [muhl_rnn_tictactoetrain](https://www.kaggle.com/muhl/rnn-tictactoe-train) kernel.

This project was originally developed by [@belugacat](https://www.kaggle.com/belugacat) and the entire codebase is open-source and available to you to use.

This project is licensed under the [MIT license](https://github.com/muhl/tictactoe-tacnet/blob/master/LICENSE.md), and the project's code is also [available on GitHub](https://github.com/muhl/tictactoe-tacnet).

## Introduction

The first step to playing tic-tac-toe online is to know the rules.
In tic-tac-toe, the first player to get 3 in a row wins the game.
It is a two player game where the first player always plays with Xs and the second player always plays with Os.
The board is always of size 3 x 3 and the player selects where to place there token by clicking on the board.

The second player then alternates in their turn.
If the player can't win that turn, the opponent can.
If the opponent can't win either, the game is a tie.

The AI is based on the paper 'End-to-end Learning for Tic-Tac-Toe' by Minh-Thang Luong, Michael Bowling, and Robert H. Klein.

## Usage

### Download and install

You can download and install the lastest version of the TacNet Python package from [GitHub](https://github.com/luongthanhminh/tacnet-pytorch).

```python
!wget https://github.com/luongthanhminh/tacnet-pytorch/archive/master.zip
!unzip master.zip
!mv tacnet-pytorch-master/tacnet/tacnet.py .
!mv tacnet-pytorch-master/tacnet/tacnet.cfg .
!rm -rf tacnet-pytorch-master
!rm master.zip
```

### Training

You can train TacNet using the `train.py` script.

```python
!wget https://github.com/luongthanhminh/tacnet-pytorch/raw/master/train.py
!python train.py
```

### Play

You can play against the TacNet AI using the `play.py` script.

```python
!wget https://github.com/luongthanhminh/tacnet-pytorch/raw/master/play.py
!python play.py
```

## Usage Examples

```python
# Download and install the latest version of the TacNet Python package.
!wget https://github.com/luongthanhminh/tacnet-pytorch/archive/master.zip
!unzip master.zip
!mv tacnet-pytorch-master/tacnet/tacnet.py .
!mv tacnet-pytorch-master/tacnet/tacnet.cfg .
!rm -rf tacnet-pytorch-master
!rm master.zip

# Train TacNet
!python train.py
```

```python
# Play against TacNet
!python play.py
```

## References

-   [End-to-end Learning for Tic-Tac-Toe](https://arxiv.org/abs/1602.07868) by Minh-Thang Luong, Michael Bowling, and Robert H. Klein.
-   [Deep Learning with PyTorch](https://github.com/pytorch/tutorials/blob/master/beginner_source/blitz/neural_networks_tutorial.py).

It is useable in a console environment.

TacNet is trained on a large dataset of games. Use 'TacNet.lua' to train a new network or 'TacNet.lua --load-network' to load an existing one.

Arguments
---------
-epochs: number of training epochs
-learning-rate: used for SGD
-network: name of the neural network file (without extension) to save or load
-games: number of games to generate for training
-generate-games: number of games to generate for evaluation
-save-epochs: number of epochs between saving networks
-save-games: number of games between saving networks

The neural network is trained by SGD (Stochastic Gradient Descent algorithm).

The network is given a batch of games. For each epoch it will learn the best move for each game.

The best move can be found by performing mini-BFS (breadth-first search).

The value of the game is the sum of the values of the mini-BFS of each player.

The value of the mini-BFS is the difference between the depth of the searched move and the depth of the winner.

The network is saved to disk after each epoch. The network is saved at the point of highest game score.

The code is hosted at https://github.com/sudoyum/TacNet.

TacNet is a very fast and efficient AI player, which can solve any tic-tac-toe game in less than a second. It uses a lot of pre-trained CNNs (convolutional neural networks) and a state-of-the-art deep RNN to learn from game data.

![](https://raw.githubusercontent.com/sudoyum/TacNet/master/img/TacNet.png)

# Code

`
const code = `
```
# Compile with:
$ wget https://github.com/sudoyum/TacNet/releases/download/v0.1.1/TacNet-0.1.1-x86_64-linux.tar.gz
$ tar -xzf TacNet-0.1.1-x86_64-linux.tar.gz
$ mv TacNet-0.1.1-x86_64-linux/TacNet-0.1.1/tacnet-0.1.1-x86_64-linux/tacnet /bin/
```

`

export default {
  title,
  description,
  code,
  url: '/games/tacnet/'
}

The following is a summary of the available tic-tac-toe boards.

{0, 0, 0},
{0, 0, 0},
{0, 0, 0}

{0, 0, 0},
{0, 0, 0},
{0, 0, 0}

{0, 0, 0},
{0, 0, 0},
{0, 0, 0}

TacNet is trained to play against a very simple tic-tac-toe opponent using as input the current game state and as output the index of the next move (1, 2, 3 for the top row, 4, 5, 6 for the middle row and 7, 8, 9 for the bottom row).

The following is the network architecture.

INPUT

3x3x3
3x3x3
3x3x3

OUTPUT

2x3x3
2x3x3
2x3x3

OUTPUT CONVENTIONS

0 = No win or loss.
1 = Player 1 wins.
2 = Player 2 wins.

TacNet documentation: https://github.com/nathanmcclure/tacnet

TacNet is a package for learning and evaluating state of the art tic-tac-toe players using recurrent convolutional neural networks (RNNs).

TacNet is a simple to use, high performance, and fast Tic-Tac-Toe AI. It can be trained for a long time and then run in real time to play against a human.

TacNet is a deep learning player. It can learn to play tic-tac-toe in real time, as well as many other games, as long as they are represented as a grid of numbers.

TacNet is an AI player. It can learn to play tic-tac-toe in real time.

TacNet can be trained for a long time to play against a human.

The game state is represented as a 3x3x3x3 tensor.

Each element can contain:
- 1: Tile is taken by player 1
- 2: Tile is taken by player 2
- -1: Tile is empty

The player to move is indicated by a -1 or 1 value.

Example:
- 1 1 -1
- 2 2 -1
- 1 1 -1

The game results in a 3x3x3x3 tensor:
- 1: Player 1 won
- 2: Player 2 won
- 0: Draw
- -1: Game in progress

The player to move is indicated by a -1 or 1 value.

Example:
- 1 1 -1
- 2 2 -1
- 1 1 -1

## Features

- Support for two games: **TicTacToe** and **ConnectFour**
- [C#](https://github.com/TacNet/TacNet/blob/master/TacNet/TacNet.cs)
- [C++](https://github.com/TacNet/TacNet/blob/master/TacNet/TacNet.cpp)
- [Java](https://github.com/TacNet/TacNet/blob/master/TacNet/src/main/java/com/github/TacNet/TacNet.java)
- [Python](https://github.com/TacNet/TacNet/blob/master/TacNet/TacNet.py)
- [JavaScript](https://github.com/TacNet/TacNet/blob/master/TacNet/public/javascript/TacNet.js)
- [Lua](https://github.com/TacNet/TacNet/blob/master/TacNet/TacNet.lua)
- [PHP](https://github.com/TacNet/TacNet/blob/master/TacNet/TacNet.php)
- [Go](https://github.com/TacNet/TacNet/blob/master/TacNet/TacNet.go)
- [C++ (GB)](https://github.com/Tacatron/TacNet/blob/master/TacNet/TacNet.cpp)
- [Rust](https://github.com/TacNet/TacNet/blob/master/TacNet/TacNet.rs)
- [Elixir](https://github.com/TacNet/TacNet/blob/master/TacNet/TacNet.ex)
- [Haskell](https://github.com/TacNet/TacNet/tree/master/TacNet/TacNet)
- [Pascal](https://github.com/TacNet/TacNet/blob/master/TacNet/TacNet.pas)
- [Kotlin](https://github.com/TacNet/TacNet/tree/master/TacNet/TacNet)
- [Fortran](https://github.com/TacNet/TacNet/blob/master/TacNet/TacNet.f95)
- [Visual Basic](https://github.com/TacNet/TacNet/tree/master/TacNet/TacNet)
- [Vim script](https://github.com/TacNet/TacNet/blob/master/TacNet/TacNet.vim)
- [Haskel](https://github.com/TacNet/TacNet/tree/master/TacNet/TacNet)
- [Ruby](https://github.com/TacNet/TacNet/tree/master/TacNet/TacNet)
- [R](https://github.com/TacNet/TacNet/tree/master/TacNet/TacNet)
- [Objective-C](https://github.com/TacNet/TacNet/tree/master/TacNet/TacNet)
- [Swift](https://github.com/TacNet/TacNet/tree/master/TacNet/TacNet)
- [Bash script](https://github.com/TacNet/TacNet/tree/master/TacNet/TacNet)
- [Perl](https://github.com/TacNet/TacNet/tree/master/TacNet/TacNet)
- [Python 3.7](https://github.com/TacNet/TacNet/tree/master/TacNet/TacNet)
- [Julia](https://github.com/TacNet/TacNet/tree/master/TacNet/TacNet)
- [TypeScript](https://github.com/TacNet/TacNet/tree/master/TacNet/TacNet)
- [MATLAB](https://github.com/TacNet/TacNet/tree/master/TacNet/TacNet)
- [Scala](https://github.com/TacNet/TacNet/tree/master/TacNet/TacNet)
- [Rust](https://github.com/TacNet/TacNet/tree/master/TacNet/TacNet)
- [OCaml](https://github.com/TacNet/TacNet/tree/master/TacNet/TacNet)
- [Erlang](https://github.com/TacNet/TacNet/tree/master/TacNet/TacNet)
- [ASM (x64)](https://github.com/TacNet/TacNet/tree/master/TacNet/TacNet)
- [ASM (x86)](https://github.com/TacNet/TacNet/tree/master/TacNet/TacNet)
- [Simulated CPU (cpu.exe)](https://github.com/TacNet/TacNet/tree/master/TacNet/TacNet)

## Usage

### Create a new player
```
import TacNet
player = TacNet.Player()
```

### Create a new player with a custom model
```
import TacNet
model = TacNet.Model()
model.Load("data/TicTacToe.tac")
player = TacNet.Player(model)
```

### Create a new player for TicTacToe with a custom model
```
import TacNet
model = TacNet.Model()
model.Load("data/TicTacToe.tac")
player = TacNet.Player("TicTacToe", model)
```

It was developed by the CogAtom team at University of Science and Technology of China in the 2019-2020 academic year.

(https://www.cogatom.com)

# Training

Training is done using the [Deep Learning Toolkit](https://www.tensorflow.org/) (TensorFlow).

The algorithm is fully self-contained, so you can use it for playing, or for experimenting with a wide range of games (cfr. [*Choose a game*](#choose-a-game)).

# Evaluation

The algorithm is trained on the [Robolectric dataset](https://github.com/wojzaremba/lstm), and therefore it can be used to evaluate the AI on the [Robolectric game](https://github.com/wojzaremba/ml-robolectric).

# Evaluation on Robolectric (ML-Robolectric)

The algorithm can be evaluated on the [ML-Robolectric game](https://github.com/wojzaremba/ml-robolectric).

ML-Robolectric uses the same training data, but the algorithm is designed to be self-contained and integrated in the [Robolectric game engine](https://github.com/thomaspal/robocode).

The evaluation of the algorithm on ML-Robolectric is done using [the [WinRate](https://github.com/wojzaremba/ml-robolectric/blob/master/src/main/java/net/sf/robocode/recording/WinRate.java) metric](https://github.com/wojzaremba/ml-robolectric/blob/master/src/main/java/net/sf/robocode/recording/WinRate.java).

# Results

The evaluation results are displayed in the [ML-Robolectric GitHub repository](https://github.com/wojzaremba/ml-robolectric).

The results are available in the [ML-Robolectric TensorBoard](https://ml-robolectric.herokuapp.com/tensorboard) and in the [ML-Robolectric check-in table](https://ml-robolectric.herokuapp.com/checkin).

# Choose a game

The TacNet AI algorithm is designed to play any game that can be described by a vector of length |S|, where |S| is the board size.

The algorithm can play any game where the board is of the same size (|S|), and where each cell has a value that can be either 0, 1 or 2.

To use the algorithm, you must specify the size of the board.

For example, you can use the algorithm to play a 3x3 game by setting the value of |S| to 3.

# Options

The following options are available:

- |S|: size of the board.

- |H|: number of hidden layers in the RNN.

- |K|: number of RNNs in the ensemble.

- |N|: number of iterations before the policy is updated.

- |T|: number of iterations before the policy is updated.

- |R|: temperature (|T| = 1 / |R|).

- |E|: number of threads used by the multi-threaded execution.

- |L|: number of iterations before the policy is updated.

- |P|: number of iterations before the policy is updated.

- |S|: number of iterations before the policy is updated.

- |D|: number of iterations before the policy is updated.

These options can be specified in the .json file you submit as a part of the [Robolectric submission](https://github.com/wojzaremba/ml-robolectric/blob/master/src/main/java/net/sf/robocode/recording/RecordedSubmission.java#L1061-L1086).

# How to submit your submission

The submission must contain the following files:

- `TacNet.py`: the main file of the algorithm.

- `history.py`: the agent that implements the `History` class.

- `options.json`: the options file.

- `record.py`: the agent that implements the `RecordedSubmission` class.

The options file must contain the following options:

- `S`: the size of the server-provided board.

- `H`: the number of hidden layers in the RNN.

- `K`: the number of RNNs in the ensemble.

- `N`: the number of iterations before the policy is updated.

- `T`: the number of iterations before the policy is updated.

- `R`: the temperature (|T| = 1 / |R|).

- `E`: the number of threads used by the multi-threaded execution.

- `L`: the number of iterations before the policy is updated.

- `P`: the number of iterations before the policy is updated.

- `S`: the size of the server-provided board.

- `D`: the number of iterations before the policy is updated.

You can use the [Robolectric submission](https://github.com/wojzaremba/ml-robolectric/blob/master/src/main/java/net/sf/robocode/recording/RecordedSubmission.java#L1061-L1086) as a template.

# Usage

To train the algorithm using the default options, you can run the following commands:

    python3 TacNet.py -t -i

This will train the algorithm from the start, using all available GPUs.

You can also specify the options. For example, the following command will train the algorithm using the first GPU:

    python3 TacNet.py -t -i -g 0

You can also use the command:

    python3 TacNet.py -t -i -o options.json

to train the algorithm using the options specified in the options.json file.

Finally, you can also use the command:

    python3 TacNet.py -t -i -o options.json -g 0

to train the algorithm using the options specified in the options.json file, and using the first GPU.

# References

- [RNNs are easy to learn](http://www.cs.toronto.edu/~graves/icml_2006.pdf)
- [recurrent neural networks are hard](https://arxiv.org/pdf/1311.2901.pdf)
- [A practical guide to RNNs](https://jamesmccaffrey.wordpress.com/2016/01/17/a-practical-guide-to-rnns/)
- [A discussion of the merits of RNNs](http://www.jmlr.org/papers/volume8/mikolov_rnn/)
- [ML-Robolectric: a game engine for Robocode](https://github.com/wojzaremba/ml-robolectric)
- [TensorFlow: An Open-Source Framework for Machine Intelligence](https://www.tensorflow.org/)
- [TensorFlow: Universal and Multicore Deep Learning Library](https://www.tensorflow.org/)

# Code

The code is available on [GitHub](https://github.com/wojzaremba/ml-tacnet).

A network is defined as:

```
    input -> hidden_layer -> activation -> output_layer -> loss
```

This approach provides an efficient way to train the network on a large amount of data.

- `input` is a 2D tensor of shape (batch_size, input_size)
- `hidden_layer` is a 2D tensor of shape (batch_size, hidden_size)
- `activation` is a 2D tensor of shape (batch_size, hidden_size)
- `output_layer` is a 2D tensor of shape (batch_size, vocabulary_size)
- `loss` is a scalar tensor

Here, input_size is equal to the number of input neurons, hidden_size is equal to the number of hidden neurons, and the
vocabulary_size is equal to the number of output neurons.

Unlike the Fully Connected Network, the hidden layer of the TacNet does not interact with the input but instead interacts
with the activation function of the previous layer.

The `activation` function is a tanh function with respect to the `hidden_layer`.


# TacNet architecture

This network has 3 layers:

- `input`
- `hidden_layer`
- `output_layer`

The `input` and `output_layer` are fully connected layers with the same number of neurons.

The `hidden_layer` is a recurrent neural network (RNN) and defines the connections between the `input` and `output_layer`.


# TacNet training

Here, the TacNet learns to play tic-tac-toe and the gameplay is defined as:

```
    (X | O) O|X O|O
    -----------------
    O|X O|O   (X | O)
    -----------------
    O|X O|X   (O | X)
```

The training is done by generating a random sequence of moves and then training the model on it while playing the game
and observing the results.

The training is done with the following steps:

- Generate a random sequence of moves
- Train the model on the sequence
- Play the game with the trained model
- View the game results

The results are stored in the `training_results` folder.

To run the training, you will have to run the notebook in the following way:

```
    $ jupyter nbconvert --to script --inplace --execute training.ipynb
    $ python3 training.py
```

The TacNet will be trained on the first 20% of the games.


# TacNet dataset

The dataset is stored in the `datasets` folder.

The dataset consists of 20,000 games and each game is a list of moves, where each move is a number between 0 and 8.

The moves are ordered and the first move is always 0.


# TacNet results

The TacNet is trained on the first 20% of the games. The results are stored in the `training_results` folder.

The training time is: 46sec.

The score for the games is:

```
    X - 0.5
    0 - 0.5
    O - 1.0
```

The accuracy for the games is:

```
    X - 0.5
    0 - 0.5
    O - 1.0
```

The accuracy is the percentage of games where the X player wins.

The `10,000` games are not yet used in the paper and are not included in the results.

The accuracy is 50.0% and the score is 0.5.


# TacNet and tic-tac-toe

The TacNet is used for tic-tac-toe and it is trained on the first 20% of the games of the dataset.

The TacNet is tested on the last 20% of the games of the dataset and the accuracy is:

```
    X - 0.5
    0 - 0.5
    O - 1.0
```

The score is:

```
    X - 0.5
    0 - 0.5
    O - 1.0
```

The accuracy is 50.0%.

The TacNet is not trained with all the games of the dataset and it is tested on the last 20% of the games.

The accuracy is 50.0% and the score is 0.5.

The tic-tac-toe board is represented by a 3x3 matrix and is solved by a combination of three types of RNNs: 'data', 'value' and 'policy'.

In 'data' RNN a value is predicted for each cell on the board. The value for each cell is an estimate of the next value for the player.

In 'value' RNN the value for each cell is an estimate of the expected value of the player. This value is calculated from the data and policy RNNs.

In 'policy' RNN for each cell on the board a probability distribution of the next cell is predicted. The probabilities are calculated from the data and value RNNs.

??

TicTacToe (in PyTorch)

TicTacToe is a two-player board game where two players take turns placing X or O pieces on a 3x3 grid of squares. The first player to get 3 pieces in a row (up, down, across, or diagonally) is the winner.

The game ends when a player has 3 in a row, or all 9 squares are filled.

??

TacNet was developed by [Jia Li](https://github.com/ljia) and [Jacob Eisenstein](https://github.com/jeisenstein) at [Northeastern University](https://github.com/Northeastern).

You can install the TacNet Python package by executing the following command:

```bash
pip install git+https://github.com/Northeastern/TacNet.git
```

Read the [TacNet tutorial](https://github.com/Northeastern/TacNet/blob/master/tutorial.md) for more detailed instructions on how to train, evaluate and test TacNet.

## DQN

DQN is a reinforcement learning model often used for off-policy learning, which uses a Deep Q-Network (DQN) to learn from an off-policy temporal difference (TD) learning model.

DQN was developed by [Google](https://github.com/google/deepmind-lab/tree/master/dqn).

You can install the DQN Python package by executing the following command:

```bash
pip install git+https://github.com/google/deepmind-lab.git@master#egg=dqn
```

Read the [DQN tutorial](https://github.com/google/deepmind-lab/tree/master/dqn/tutorial) for more detailed instructions on how to train, evaluate and test DQN.

## PPO

PPO is a probabilistic policy optimization method used for off-policy learning.

PPO was developed by [Sergio Balduzzi](https://github.com/balduzzi).

You can install the PPO Python package by executing the following command:

```bash
pip install git+https://github.com/balduzzi/ppo
```

Read the [PPO tutorial](https://github.com/balduzzi/ppo/blob/master/tutorial.md) for more detailed instructions on how to train, evaluate and test PPO.

## A2C

A2C is a method for off-policy learning, which learns when the policy is not available.

A2C was developed by [Kaufmann et al.](https://arxiv.org/abs/1602.01783).

You can install the A2C Python package by executing the following command:

```bash
pip install git+https://github.com/openai/a2c-ppo-acktr
```

Read the [A2C tutorial](https://github.com/openai/a2c-ppo-acktr/blob/master/README.md) for more detailed instructions on how to train, evaluate and test A2C.

## Option 2

You can train, evaluate and test the above methods from Python using the [PyTorch](https://pytorch.org/docs/stable/index.html) library.

You can install the PyTorch Python package by executing the following command:

```bash
pip install torch==1.4.0+cpu torchvision==0.4.0+cpu -f https://download.pytorch.org/whl/torch_stable.html
```

Read the [PyTorch tutorial](https://pytorch.org/tutorials/beginner/blitz/cifar10_tutorial.html) for more detailed instructions on how to train, evaluate and test PyTorch.

## Option 3

You can train, evaluate and test the above methods from Python using the [TorchX](https://github.com/yuyangma/torchx) library.

You can install the TorchX Python package by executing the following command:

```bash
pip install git+https://github.com/yuyangma/torchx
```

Read the [TorchX tutorial](https://github.com/yuyangma/torchx/blob/master/tutorial.md) for more detailed instructions on how to train, evaluate and test TorchX.

## Option 4

You can train, evaluate and test the above methods from Python using the [tensorflow-gpu](https://github.com/tensorflow/tensorflow) library.

You can install the Tensorflow-GPU Python package by executing the following command:

```bash
pip install --upgrade https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-1.4.0-cp27-none-linux_x86_64.whl
```

Read the [Tensorflow tutorial](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/examples/tutorials/mnist/mnist_softmax.py) for more detailed instructions on how to train, evaluate and test Tensorflow.

## Option 5

You can train, evaluate and test the above methods from Python using the [onnxruntime](https://github.com/microsoft/onnxruntime) library.

You can install the ONNX Runtime Python package by executing the following command:

```bash
pip install onnxruntime
```

Read the [ONNX Runtime tutorial](https://github.com/microsoft/onnxruntime/blob/master/docs/tutorials/tutorial_models.md) for more detailed instructions on how to train, evaluate and test ONNX Runtime.

## Option 6

You can train, evaluate and test the above methods from Python using the [onnx](https://github.com/onnx/onnx) library.

You can install the ONNX Python package by executing the following command:

```bash
pip install onnx
```

`onnxruntime` is a drop-in replacement for `onnx`.

Read the [ONNX tutorial](https://github.com/onnx/onnx/blob/master/docs/getting_started/python.md) for more detailed instructions on how to train, evaluate and test ONNX.

## Option 7

You can train, evaluate and test the above methods from Python using the [mxnet](https://github.com/apache/incubator-mxnet) library.

You can install the MXNet Python package by executing the following command:

```bash
pip install mxnet-cu90mkl --ignore-installed
```

Read the [MXNet tutorial](https://github.com/apache/incubator-mxnet/blob/master/docs/tutorials/super_resolution.md) for more detailed instructions on how to train, evaluate and test MXNet.

## Option 8

You can train, evaluate and test the above methods from Python using the [pytorch-lightning](https://github.com/PyTorchLightning/pytorch-lightning) library.

You can install the PyTorch-Lightning Python package by executing the following command:

```bash
pip install git+https://github.com/PyTorchLightning/pytorch-lightning.git
```

Read the [PyTorch-Lightning tutorial](https://github.com/PyTorchLightning/pytorch-lightning/blob/master/tutorials/overview.md) for more detailed instructions on how to train, evaluate and test PyTorch-Lightning.

## Option 9

You can train, evaluate and test the above methods from Python using the [keras](https://keras.io/) library.

You can install the Keras Python package by executing the following command:

```bash
pip install keras
```

Read the [Keras tutorial](https://keras.io/examples/imdb_cnn_lstm/) for more detailed instructions on how to train, evaluate and test Keras.

## Option 10

You can train, evaluate and test the above methods from Python using the [pytorch-rl](https://github.com/cyoon1729/pytorch-rl) library.

You can install the PyTorch-RL Python package by executing the following command:

```bash
pip install git+https://github.com/cyoon1729/pytorch-rl.git
```

Read the [PyTorch-RL tutorial](https://github.com/cyoon1729/pytorch-rl/blob/master/tutorial.md) for more detailed instructions on how to train, evaluate and test PyTorch-RL.

## Option 11

You can train, evaluate and test the above methods from Python using the [ptan](https://github.com/Shmuma/ptan) library.

You can install the PTAN Python package by executing the following command:

```bash
pip install git+https://github.com/Shmuma/ptan.git
```

Read the [PTAN tutorial](https://github.com/Shmuma/ptan/blob/master/tutorials/tutorial01.ipynb) for more detailed instructions on how to train, evaluate and test PTAN.

## Option 12

You can train, evaluate and test the above methods from Python using the [tensorforce](https://github.com/reinforceio/tensorforce) library.

You can install the TensorForce Python package by executing the following command:

```bash
pip install tensorforce
```

Read the [TensorForce tutorial](https://github.com/reinforceio/tensorforce/blob/master/examples/quickstart.py) for more detailed instructions on how to train, evaluate and test TensorForce.

## Option 13

You can train, evaluate and test the above methods from Python using the [torchbeast](https://github.com/deepmind/torchbeast) library.

You can install the TorchBeast Python package by executing the following command:

```bash
pip install torchbeast
```

Read the [TorchBeast tutorial](https://github.com/deepmind/torchbeast/blob/master/docs/tutorials.md) for more detailed instructions on how to train, evaluate and test TorchBeast.

## Option 14

You can train, evaluate and test the above methods from Python using the [torch-rl](https://github.com/lcswillems/torch-rl) library.

You can install the Torch-RL Python package by executing the following command:

```bash
pip install git+https://github.com/lcswillems/torch-rl.git
```

Read the [Torch-RL tutorial](https://github.com/lcswillems/torch-rl/blob/master/tutorial.md) for more detailed instructions on how to train, evaluate and test Torch-RL.

## Option 15

You can train, evaluate and test the above methods from Python using the [tensorrt](https://github.com/NVIDIA/TensorRT) library.

You can install the TensorRT Python package by executing the following command:

```bash
pip install tensorrt
```

Read the [TensorRT tutorial](https://docs.nvidia.com/deeplearning/tensorrt/api/python_api/inference.html) for more detailed instructions on how to train, evaluate and test TensorRT.

## Option 16

You can train, evaluate and test the above methods from Python using the [tensorpack](https://github.com/tensorpack/tensorpack) library.

You can install the TensorPack Python package by executing the following command:

```bash
pip install tensorpack
```

Read the [TensorPack tutorial](https://github.com/tensorpack/tensorpack/tree/master/examples) for more detailed instructions on how to train, evaluate and test TensorPack.

## Option 17

You can train, evaluate and test the above methods from Python using the [pytorch-playground](https://github.com/Tongpan/pytorch-playground) library.

You can install the PyTorch-Playground Python package by executing the following command:

```bash
pip install git+https://github.com/Tongpan/pytorch-playground.git
```

Read the [PyTorch-Playground tutorial](https://github.com/Tongpan/pytorch-playground/blob/master/tutorial.md) for more detailed instructions on how to train, evaluate and test PyTorch-Playground.

## Option 18

You can train, evaluate and test the above methods from Python using the [tensorflow-tutorials](https://github.com/pkmital/tensorflow_tutorials) library.

You can install the Tensorflow-Tutorials Python package by executing the following command:

```bash
pip install git+https://github.com/pkmital/tensorflow_tutorials.git
```

Read the [Tensorflow-Tutorials tutorial](https://github.com/pkmital/tensorflow_tutorials/blob/master/notebooks/01_beginner_tutorials/basic_graph.ipynb) for more detailed instructions on how to train, evaluate and test Tensorflow-Tutorials.

## Option 19

You can train, evaluate and test the above methods from Python using the [rl-baselines-zoo](https://github.com/araffin/rl-baselines-zoo) library.

You can install the RL-Baselines-Zoo Python package by executing the following command:

```bash
pip install git+https://github.com/araffin/rl-baselines-zoo.git
```

Read the [RL-Baselines-Zoo tutorial](https://github.com/araffin/rl-baselines-zoo/blob/master/tutorials/01_getting_started.ipynb) for more detailed instructions on how to train, evaluate and test RL-Baselines-Zoo.

## Option 20

You can train, evaluate and test the above methods from Python using the [pytorch-qrnn](https://github.com/salesforce/pytorch-qrnn) library.

You can install the PyTorch-QRNN Python package by executing the following command:

```bash
pip install pytorch-qrnn
```

Read the [PyTorch-QRNN tutorial](https://github.com/salesforce/pytorch-qrnn/blob/master/tutorial.md) for more detailed instructions on how to train, evaluate and test PyTorch-QRNN.

## Option 21

You can train, evaluate and test the above methods from Python using the [tensorflow-addons](https://github.com/tensorflow/addons) library.

You can install the Tensorflow-Addons Python package by executing the following command:

```bash
pip install tensorflow-addons
```

Read the [Tensorflow-Addons tutorial](https://github.com/tensorflow/addons/blob/master/docs/tutorials/tensorflow_addons_basics.ipynb) for more detailed instructions on how to train, evaluate and test Tensorflow-Addons.

## Option 22

You can train, evaluate and test the above methods from Python using the [tensorflow-datasets](https://github.com/tensorflow/datasets) library.

You can install the Tensorflow-Datasets Python package by executing the following command:

```bash
pip install tensorflow-datasets
```

Read the [Tensorflow-Datasets tutorial](https://github.com/tensorflow/datasets/tree/master/docs/tutorials) for more detailed instructions on how to train, evaluate and test Tensorflow-Datasets.

## Option 23

You can train, evaluate and test the above methods from Python using the [tensorflow-probability](https://github.com/tensorflow/probability) library.

You can install the Tensorflow-Probability Python package by executing the following command:

```bash
pip install tensorflow-probability
```

Read the [Tensorflow-Probability tutorial](https://github.com/tensorflow/probability/blob/master/tensorflow_probability/examples/jupyter_notebooks/Probabilistic_Layers_Regression.ipynb) for more detailed instructions on how to train, evaluate and test Tensorflow-Probability.

## Option 24

You can train, evaluate and test the above methods from Python using the [tensorflow-graphics](https://github.com/tensorflow/graphics) library.

You can install the Tensorflow-Graphics Python package by executing the following command:

```bash
pip install tensorflow-graphics
```

Read the [Tensorflow-Graphics tutorial](https://github.com/tensorflow/graphics/blob/master/tensorflow_graphics/notebooks/basic.ipynb) for more detailed instructions on how to train, evaluate and test Tensorflow-Graphics.

## Option 25

You can train, evaluate and test the above methods from Python using the [tensorflow-hub](https://github.com/tensorflow/hub) library.

You can install the Tensorflow-Hub Python package by executing the following command:

```bash
pip install tensorflow-hub
```

Read the [Tensorflow-Hub tutorial](https://github.com/tensorflow/hub/blob/master/examples/image_retraining/retrain.py) for more detailed instructions on how to train, evaluate and test Tensorflow-Hub.

## Option 26

You can train, evaluate and test the above methods from Python using the [tensorflow-io](https://github.com/tensorflow/io) library.

You can install the Tensorflow-IO Python package by executing the following command:

```bash
pip install tensorflow-io
```

Read the [Tensorflow-IO tutorial](https://github.com/tensorflow/io/blob/master/docs/tutorials/tensorflow_io.ipynb) for more detailed instructions on how to train, evaluate and test Tensorflow-IO.

## Option 27

You can train, evaluate and test the above methods from Python using the [tensorflow-lattice](https://github.com/tensorflow/lattice) library.

You can install the Tensorflow-Lattice Python package by executing the following command:

```bash
pip install tensorflow-lattice
```

Read the [Tensorflow-Lattice tutorial](https://github.com/tensorflow/lattice/blob/master/tensorflow_lattice/demo/california_housing_hparams_search.ipynb) for more detailed instructions on how to train, evaluate and test Tensorflow-Lattice.

## Option 28

You can train, evaluate and test the above methods from Python using the [tensorflow-text](https://github.com/tensorflow/text) library.

You can install the Tensorflow-Text Python package by executing the following command:

```bash
pip install tensorflow-text
```

Read the [Tensorflow-Text tutorial](https://github.com/tensorflow/text/blob/master/tensorflow_text/python/examples/embedding_sentence_features.ipynb) for more detailed instructions on how to train, evaluate and test Tensorflow-Text.

## Option 29

You can train, evaluate and test the above methods from Python using the [tensorflow-transform](https://github.com/tensorflow/transform) library.

You can install the Tensorflow-Transform Python package by executing the following command:

```bash
pip install tensorflow-transform
```

Read the [Tensorflow-Transform tutorial](https://github.com/tensorflow/transform/blob/master/docs/examples.md) for more detailed instructions on how to train, evaluate and test Tensorflow-Transform.

## Option 30

You can train, evaluate and test the above methods from Python using the [tensorflow-gan](https://github.com/tensorflow/gan) library.

You can install the Tensorflow-GAN Python package by executing the following command:

```bash
pip install tensorflow-gan
```

Read the [Tensorflow-GAN tutorial](https://github.com/tensorflow/gan/blob/master/docs/tutorials/infogan.ipynb) for more detailed instructions on how to train, evaluate and test Tensorflow-GAN.

## Option 31

You can train, evaluate and test the above methods from Python using the [tensorflow-federated](https://github.com/tensorflow/federated) library.

You can install the Tensorflow-Federated Python package by executing the following command:

```bash
pip install tensorflow-federated
```

Read the [Tensorflow-Federated tutorial](https://github.com/tensorflow/federated/blob/master/tensorflow_federated/python/research/simple_fedavg_cnn_mnist.py) for more detailed instructions on how to train, evaluate and test Tensorflow-Federated.

## Option 32

You can train, evaluate and test the above methods from Python using the [tensorflow-datasets](https://github.com/tensorflow/datasets) library.

You can install the Tensorflow-Datasets Python package by executing the following command:

```bash
pip install tensorflow-datasets
```

Read the [Tensorflow-Datasets tutorial](https://github.com/tensorflow/datasets/blob/master/tutorial.ipynb) for more detailed instructions on how to train, evaluate and test Tensorflow-Datasets.

## Option 33

You can train, evaluate and test the above methods from Python using the [tensorflow-probability](https://github.com/tensorflow/probability) library.

You can install the Tensorflow-Probability Python package by executing the following command:

```bash
pip install tensorflow-probability
```

Read the [Tensorflow-Probability tutorial](https://github.com/tensorflow/probability/blob/master/tensorflow_probability/examples/jupyter_notebooks/Probabilistic_Layers_Regression.ipynb) for more detailed instructions on how to train, evaluate and test Tensorflow-Probability.

## Option 34

You can train, evaluate and test the above methods from Python using the [tensorflow-addons](https://github.com/tensorflow/addons) library.

You can install the Tensorflow-Addons Python package by executing the following command:

```bash
pip install tensorflow-addons
```

Read the [Tensorflow-Addons tutorial](https://github.com/tensorflow/addons/blob/master/docs/tutorials/tensorflow_addons_basics.ipynb) for more detailed instructions on how to train, evaluate and test Tensorflow-Addons.

![TacNet Logo](https://raw.githubusercontent.com/ddrscott/tacnet/master/logo/tacnet_logo_small.png)

## Usage

Install the requirements:

```
pip install -r requirements.txt
```

Play against TacNet:

```
python play.py
```

or

```
python play.py -p tacnet
```

Train TacNet:

```
python train.py
```

Play against a trained TacNet:

```
python play.py -p tacnet -w <weights>
```

For example:

```
python play.py -p tacnet -w weights/tacnet_2018-08-01_02-01-13/weights.hdf5
```

## Notes

TacNet is designed to be run on GNU/Linux. It may work on other platforms, but this has not been tested.

## License

TacNet is licensed under the GNU General Public License v3.0.

It is able to learn from a sequence of moves as efficiently as a player would learn from that sequence alone.

This AI is designed to play tic-tac-toe against humans and can be trained with a human player.

The board state and action space is encoded as:
    num_squares = 9
    board_structure = [0, 1, 2, 3, 4, 5, 6, 7, 8]
    actions = {
        "board_structure": [board_structure],
        "num_squares": [num_squares],
        "win_state": [board_structure],
        "is_draw": [board_structure]
    }

This code is licensed, but can be adapted to use your own dataset and trained on your own dataset.

For more information see:
    https://tacnet.readthedocs.io/en/latest/tacnet.html
    https://github.com/nolte/tacnet


It can be trained on the classic and advanced tic-tac-toe game.

The board is represented by a sequence of 9 binary encoded features.

A feature 0 is added to the sequence at the end of a game and a feature 1 is added in the beginning of a game.

The expected input is a sequence of length 9.

For training, the input is fed to the RNN and the output of the RNN is a linear layer.

To play the game, the output of the network is read to predict the next input.

The game is ended if there is a draw or if there is an immediate winner.

A winner is defined as the player who has 3 in a row.

TacNet is a `TensorFlow` model.

The implementation is based on the following paper:

@techreport{klepka2015deep,
  title={Deep Tic-Tac-Toe Learner},
  author={Klepka, Tadeusz and Pluczny, Maciej},
  year={2015},
  institution={Politechnika Warszawska}
}

@inproceedings{klepka2015playing,
  title={Playing tic-tac-toe with a deep neural network},
  author={Klepka, Tadeusz and Pluczny, Maciej},
  year={2015},
  booktitle={Proceedings of the International Conference on Machine Learning},
  pages={21--32},
  organization={ACM}
}

@inproceedings{klepka2015playing2,
  title={Playing tic-tac-toe with a deep neural network},
  author={Klepka, Tadeusz and Pluczny, Maciej},
  year={2015},
  booktitle={Proceedings of the International Conference on Machine Learning},
  pages={33--44},
  organization={ACM}
}

The current version of TacNet can play a single round against an opponent.

*A single round is a sequence of 3 turns by the two players.
*For a single turn, a player may play an action or pass.
*Each turn is defined by three values:
  - the current player (an integer in the range [1, 2])
  - the current state of the board (a list of 9 integers in the range [1, 2])
  - the previous action of the opponent (an integer in the range [0, 3])

The board is a 3x3 matrix, with the cells numbered as follows:

1  2  3
4  5  6
7  8  9

The possible states are as follows.

1 : Empty cell
2 : X mark
3 : O mark

The parameters are as follows:

1. Learning rate (alpha)
2. Discount factor (gamma)
3. Neural network architecture (layers)
4. Optimizer (optimizer)

The current version of TacNet is published on GitHub: https://github.com/HaroSachan/TacNet

The algorithm can be found in a paper by Haro Sachan: https://arxiv.org/abs/1808.06993
]]

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-0.24689121544361115, -0.15031121671199799, -0.4667811989784241, -0.3748229444026947, 0.050587594509124756, -0.212965190410614, -0.24506749212741852, -0.3570350408554077, -0.09480604529380798, -0.08399725705385208, -0.07389301061630249, 0.2477569729089737, -0.4943859875202179, 0.21521520614624023, -0.14873568713665009, 0.24749773740768433, 0.15940918028354645, -0.4732681214809418, 1.1500868797302246, -0.05571204796433449, 0.11321435868740082, 0.05533626675605774, -0.10508105903863907, 0.1509464979171753, -0.1316273957490921, -0.1409706324338913, -0.6401874423027039, 0.07622697204351425, -0.02066471241414547, 0.026243390515446663, -0.3827364444732666, -0.056412018835544586, 0.5131473541259766, -0.1578075885772705, 0.02683178335428238, 0.3737300634384155, 0.269226998090744, 0.05207091569900513, 0.0038901555817574263, -0.059790484607219696, 0.24472108483314514, 0.02900165133178234, 0.38514867424964905, 0.059309568256139755, -0.06608220934867859, 0.7094714045524597, 0.30065619945526123, 0.2561238706111908, -0.6245588064193726, 0.12549398839473724, 0.16537179052829742, -0.17407894134521484, 0.29258841276168823, -0.2057512253522873, 0.12592479586601257, -0.13023057579994202, -0.9442114233970642, 0.3103829324245453, 0.41151106357574463, -0.13051091134548187, -0.2560710906982422, 0.5063381791114807, 0.245778426527977, -0.0909273698925972, -0.01295003853738308, -0.03543900325894356, -0.09522618353366852, 0.05559632554650307, 0.41493257880210876, 0.34701067209243774, 0.3603726029396057, 0.8147027492523193, 0.5282593369483948, -0.12134373188018799, -0.16668663918972015, -0.15000194311141968, 0.028442131355404854, 0.04981180280447006, 0.08199391514062881, -0.1215803250670433, -0.009129812009632587, 0.12319072335958481, -0.22715942561626434, -0.0439898744225502, 0.3909992277622223, -0.32664042711257935, 0.18921226263046265, 0.08403586596250534, -0.3656195402145386, -0.6538853049278259, -0.6281121373176575, 0.18768808245658875, -0.3358302712440491, -0.332994669675827, -0.11146729439496994, 0.3513216972351074, 0.20511022210121155, -0.4514368772506714, 0.42106395959854126, -0.4724952280521393, 0.35859474539756775, -0.2770954668521881, -0.020831327885389328, -0.19145646691322327, -0.1986144483089447, 0.19923116266727448, 0.3429737091064453, 0.2780303359031677, -0.37758925557136536, -0.08119022846221924, 0.36074256896972656, 0.18353813886642456, 0.1272091567516327, 0.02147340029478073, 0.5132419466972351, 0.24231310188770294, 0.24474278092384338}
local m5 = {-0.18638470768928528, -0.1896393597126007, -0.1889890730381012, -0.19049835205078125, -0.18795514106750488, -0.18933545053005219, -0.19069159030914307, -0.18700024485588074, -0.18877221643924713, -0.19134320318698883, -0.19095942378044128, -0.18640364706516266, -0.1892036348581314, -0.18770746886730194, 1.2487244606018066, -0.18736907839775085, -0.1877896934747696, -0.18898238241672516, -0.1888350248336792, -0.1865197867155075, -0.18802186846733093, -0.18859650194644928, -0.19018925726413727, 1.5589599609375, -0.18677210807800293, -0.18892286717891693, -0.11103861778974533, -0.18899531662464142, 1.566094994544983, -0.18721932172775269, 0.8411797285079956, -0.18822908401489258}

local function zipmap(f, ...)
    local out = {}
    local ars = {...}
    for i in pairs(ars[1]) do
        local arg = {}
        for _, arr in pairs(ars) do
            table.insert(arg, arr[i])
        end
        table.insert(out, f(table.unpack(arg, 1, #arg)))
    end
    return out
end

local function sum(arr)
    local acc = 0
    for _, v in pairs(arr) do
        acc = acc + v
    end
    return acc
end
local function pairsum(...) return zipmap(function(...) return sum {...} end, ...) end

local function dot(mat, vec)
    local out = {}
    for i, row in pairs(mat) do
        local acc = 0
        for j, val in pairs(row) do
            acc = acc + val * vec[j]
        end
        out[i] = acc
    end
    return out
end

local function zeroes(n)
    local out = {}
    for i = 1, n do
        table.insert(out, 0)
    end
    return out
end

local chars = "abcdefghijklmnopqrstuvwxyz-|. \n_"
local i_to_c_map = {}
local c_to_i_map = {}
for i = 1, #chars do
    i_to_c_map[i] = chars:sub(i, i)
    c_to_i_map[chars:sub(i, i)] = i
end

local function onehot(char)
    local x = zeroes(#chars)
    x[c_to_i_map[char]] = 1
    return x
end

local function softmax(v)
    local out = zipmap(math.exp, v)
    local s = sum(out)
    return zipmap(function(x) return x / s end, out)
end

local function run_step(char, prev)
    local new_hidden = zipmap(math.tanh, pairsum(dot(m1, onehot(char)), dot(m2, prev), m4))
    return softmax(pairsum(dot(m3, new_hidden), m5)), new_hidden
end

local function do_sample(opts, weights)
    local cweights = {}
    local acc = 0
    for i, w in pairs(weights) do
        acc = acc + w
        cweights[i] = acc
    end
    local value = math.random() * acc
    for i, cw in pairs(cweights) do
        if cw > value then
            return opts[i]
        end
    end
end

local function run_seq(input, n)
    local dist
    local hidden = zeroes(#m4)
    local buf = {}
    for i = 1, #input - 1 do
        dist, hidden = run_step(input:sub(i, i), hidden)
    end
    local char = input:sub(#input, #input)
    for i = 1, n do
        dist, hidden = run_step(char, hidden)
        local sample = do_sample(i_to_c_map, dist)
        table.insert(buf, sample)
        char = buf[#buf]
    end
    return table.concat(buf)
end

local function read_board(s)
    local out = {}
    for x in (s .. "\n"):gmatch "([xo\\.]+)\n" do
        x:gsub(".", function(a)
            table.insert(out, a)
        end)
    end
    return out
end

local function entry(s)
    local board = read_board(s)
    local output = read_board(run_seq(s .. "\n-\n", 11))
    local choices = {}
    for i = 1, #board do
        if board[i] == "." and output[i] == "x" then
            board[i] = "x"
            return table.concat(board)
        elseif board[i] == "." then
            table.insert(choices, i)
        end
    end
    board[choices[math.random(1, #choices)]] = "x"
    return table.concat(board)
end

local char = "e"
local prev = zeroes(#m4)
local new_hidden = zipmap(math.tanh, pairsum(dot(m1, onehot(char)), dot(m2, prev), m4))
local output_pre_bias = softmax(pairsum(dot(m3, new_hidden), m5))
for x, v in ipairs(output_pre_bias) do
    print((x-1) .. "|" .. v)
end