RESEARCH

STARDATA: a StarCraft AI Research Dataset

October 5, 2017

Abstract

We release a dataset of 65646 StarCraft replays that contains 1535 million frames and 496 million player actions. We provide full game state data along with the original replays that can be viewed in StarCraft. The game state data was recorded every 3 frames which ensures suitability for a wide variety of machine learning tasks such as strategy classification, inverse reinforcement learning, imitation learning, forward modeling, partial information extraction, and others. We use TorchCraft to extract and store the data, which standardizes the data format for both reading from replays and reading directly from the game. Furthermore, the data can be used on different operating systems and platforms. The dataset contains valid, non-corrupted replays only and its quality and diversity was ensured by a number of heuristics. We illustrate the diversity of the data with various statistics and provide examples of tasks that benefit from the dataset. We make the dataset available here. En Taro Adun!

Download the Paper

Related Publications

March 02, 2020

SYSTEMS RESEARCH

Federated Optimization in Heterogenous Networks | Facebook AI Research

Federated Learning is a distributed learning paradigm with two key challenges that differentiate it from traditional distributed optimization: (1) significant variability in terms of the systems characteristics on each device in the network…

Tian Li, Anit Kumar Sahu, Manzil Zaheer, Maziar Sanjabi, Ameet Talwalkar, Virginia Smith

March 02, 2020

November 10, 2019

Adversarial Bandits with Knapsacks | Facebook AI Research

We consider Bandits with Knapsacks (henceforth, BwK), a general model for multi-armed bandits under supply/budget constraints. In particular, a bandit algorithm needs to solve a well-known knapsack problem: find an optimal packing of items into…

Nicole Immorlica, Karthik Abinav Sankararaman, Robert Schapire, Aleksandrs Slivkins

November 10, 2019

November 10, 2019

NLP

Adversarial Bandits with Knapsacks | Facebook AI Research

We consider Bandits with Knapsacks (henceforth, BwK), a general model for multi-armed bandits under supply/budget constraints. In particular, a bandit algorithm needs to solve a well-known knapsack problem: find an optimal packing of items into…

Nicole Immorlica, Karthik Abinav Sankararaman, Robert Schapire, Aleksandrs Slivkins

November 10, 2019

November 05, 2019

COMPUTER VISION

NLP

CLUTRR: A Diagnostic Benchmark for Inductive Reasoning from Text | Facebook AI Research

The recent success of natural language understanding (NLU) systems has been troubled by results highlighting the failure of these models to generalize in a systematic and robust way. In this work, we introduce a diagnostic benchmark suite,…

Koustuv Sinha, Shagun Sodhani, Jin Dong, Joelle Pineau, William L. Hamilton

November 05, 2019

Help Us Pioneer The Future of AI

We share our open source frameworks, tools, libraries, and models for everything from research exploration to large-scale production deployment.