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---
license: apache-2.0
dataset_info:
features:
- name: version
dtype: int64
- name: id
dtype: string
- name: mapWidth
dtype: int64
- name: mapHeight
dtype: int64
- name: usernames
list: string
- name: stars
list: int64
- name: cities
list: int64
- name: cityArmies
list: int64
- name: generals
list: int64
- name: mountains
list: int64
- name: moves
list:
list: int64
splits:
- name: train
num_bytes: 424747643
num_examples: 18803
download_size: 61449177
dataset_size: 424747643
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
tags:
- games
- replays
- generalsio
---
# ⚔️ Generals.io High-Rank Replay Dataset 🌟
## Overview
This dataset contains a curated collection of 1v1 game replays from the online strategy game [generals.io](httphttp://generals.io), specifically designed for training high-level reinforcement learning agents 🤖.
* 🏆 **High-Quality Matches:** Includes games where at least one participant had a star rating of **70 or higher** 📈, ensuring a baseline of quality and strategic depth.
* ✅ **Clean Data:** Carefully filtered to remove outliers, games with AFK players, and trolls, providing a clean and focused dataset for agent training.
* 🚀 **Proven Effectiveness:** An agent trained exclusively on this dataset has been shown to be capable of reaching a **60-star rating**, demonstrating the sufficiency and quality of the provided replays.
### Game Version
All replays were recorded on the game patch featuring an **alternating priority** turn system. However, extensive testing has shown that agents trained on this data generalize effectively to the newer priority system without requiring further training.
## Dataset Structure & Documentation
The dataset is composed of individual JSON files, where each file represents a single game replay. For a detailed technical description of the replay format, please refer to the official (archived) documentation:
➡️ **[http://dev.generals.io/replays (via Archive.org)](https://web.archive.org/web/20190917202205/http://dev.generals.io/replays)**
The structure of each JSON object is as follows:
| Field | Type | Description |
|---------------|---------------------------|---------------------------------------------------------------------------------------------------------|
| `version` | `Integer` | The version number of the replay format. |
| `id` | `String` | A unique identifier for the replay. |
| `mapWidth` | `Integer` | The width of the game map in tiles. |
| `mapHeight` | `Integer` | The height of the game map in tiles. |
| `usernames` | `List[String]` | A list of the usernames for the players in the game. The index corresponds to the player's turn order. |
| `stars` | `List[Integer]` | The star rating of each player at the time the game was played. |
| `cities` | `List[Integer]` | A list of tile indices representing the locations of all neutral cities at the start of the game. |
| `cityArmies` | `List[Integer]` | The initial army count for each corresponding city in the `cities` list. |
| `generals` | `List[Integer]` | The starting tile indices for each player's general (home base). |
| `mountains` | `List[Integer]` | A list of tile indices for all impassable mountain tiles on the map. |
| `moves` | `List[List[Integer]]` | The core sequence of game actions. Each inner list is a move tuple, typically representing `[player_index, start_tile, end_tile, is_50%_move, turn_number]`. |
| `afks` | `List[]` | A list that would contain information on AFK players. This has been filtered and will always be empty. |
## Usage Example
This dataset is hosted on the Hugging Face Hub and can be loaded directly using the `datasets` library. Here is a showcase of how to load the dataset and inspect its contents.
```python
from datasets import load_dataset
# 1. Load the dataset directly from the Hugging Face Hub
# You may need to log in first: huggingface-cli login
print("Loading dataset...")
dataset = load_dataset("strakammm/generals_io_replays")
# The dataset is loaded into a DatasetDict, we'll use the 'train' split
train_dataset = dataset['train']
# 2. Print the total number of replays in the dataset
print(f"\nTotal number of replays: {len(train_dataset)}")
# 3. Iterate over the first few replays to showcase the data
print("\n--- Showcasing first 5 replays ---")
for i in range(5):
replay = train_dataset[i]
# Get the players and the number of moves
players = replay['usernames']
num_moves = len(replay['moves'])
print(f"\nReplay {i+1}:")
print(f" Players: {players[0]} vs {players[1]}")
print(f" Total moves: {num_moves}")
print("\n------------------------------------")
```
## Citation
If you use this dataset in your research, please cite the following paper:
```bibtex
@misc{generals_rl,
author = {Matej Straka, Martin Schmid},
title = {Artificial Generals Intelligence: Mastering Generals.io with Reinforcement Learning},
year = {2025},
eprint = {2507.06825},
archivePrefix = {arXiv},
primaryClass = {cs.LG},
} |