https://github.com/achoum/exploratron

Exploratron is a open-source turn-by-turn sandbox puzzle game with multiple simple mechanics that interact with each other and create complex and interesting emergent gameplay situations.
https://github.com/achoum/exploratron

genetic-algorithm machine-learning puzzle roguelike sandbox simulation

Last synced: about 2 months ago
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Exploratron is a open-source turn-by-turn sandbox puzzle game with multiple simple mechanics that interact with each other and create complex and interesting emergent gameplay situations.

• Host: GitHub
• URL: https://github.com/achoum/exploratron
• Owner: achoum
• Created: 2022-06-19T19:53:02.000Z (over 2 years ago)
• Default Branch: main
• Last Pushed: 2022-12-26T10:01:45.000Z (almost 2 years ago)
• Last Synced: 2024-10-07T05:41:05.016Z (3 months ago)
• Topics: genetic-algorithm, machine-learning, puzzle, roguelike, sandbox, simulation
• Language: C++
• Homepage: https://achoum.github.io/exploratron
• Size: 5.46 MB
• Stars: 4
• Watchers: 2
• Forks: 0
• Open Issues: 1
• Metadata Files:
  • Readme: README.md
  • Changelog: CHANGELOG.md

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README

        
# Exploratron

**Exploration** is a open-source turn-by-turn sandbox puzzle game with multiple simple mechanics that interact with each other and create complex and interesting emergent gameplay situations.

![](docs/game.gif)

Exploration is compatible with Linux, Windows and Web browser (using Webassembly).

You can play the Web browser version [here](https://achoum.github.io/exploratron).

## Compilation from source

Requires _Bazel_.

On Web browser:

```shell

./tools/game_wasm.sh

```

On Linux:

```shell

./tools/game_terminal.sh

```

On Windows:

```shell

tools\game_terminal.bat

```

## Map creation

Maps are created using [Tiled](https://www.mapeditor.org/download.html). See examples in the `exploratron\assets\map` directory.

## Agent training

Exploratron is also a framework to train and evaluate agent using Machine Learning.

![](docs/agent_1.gif)

Read, modify and and run `./tools/train_agent.sh` to train and evaluate agents.

Some examples of commands (see `./tools/train_agent.sh` for details):

```shell

LOGDIR=$(pwd)/training_logs

FLAG="--config=linux --define=terminal=linux_console"

# MODE="-c dbg"

MODE="-c opt"

# Run the evaluation on the "Gather" area using the Random controler (no leaning).

bazel run $MODE --config=linux --define=terminal=linux_console //exploratron/cli:evaluate_main $FLAG -- \

    --arena=Gather --controller_key=Random --num_repetitions=100

# Train and evaluate a genetic controller on the "Gather" area.

# Hyper-parameters are available in `exploratron/controller/genetic/genetic.h`

bazel run $MODE --config=linux //exploratron/controller/genetic:train_main $FLAG -- \

    --training_log_base=${LOGDIR}/gather/genetic/r1_

# Train and evaluate a hill climbing controller on the "Gather" area.

# Hyper-parameters are available in `exploratron/controller/hill_climb/hill_climb.h`

bazel run $MODE --config=linux //exploratron/controller/hill_climbing:train_main $FLAG -- \

    --training_log_base=${LOGDIR}/gather/hill_climb/r1_

# Run the evaluation on the "Gather" area using the keyboard controler (no leaning).

bazel run $MODE --config=linux //exploratron/cli:evaluate_main $FLAG -- \

    --arena=Gather --controller_key=Keyboard

```

Results can be compared using the R scipe `./tools/plot_training_logs.R`.