playing-games

Starcraft II

3260 papers • 126 benchmarks • 313 datasets

Starcraft II is a RTS game; the task is to train an agent to play the game. ( Image credit: The StarCraft Multi-Agent Challenge )

(Image credit: Papersgraph)

Benchmarks

These leaderboards are used to track progress in starcraft-ii

Trend

Dataset

Best Model

Actions

MoveToBeacon

CollectMineralShards

SMAC-Exp

Libraries

i

Use these libraries to find starcraft-ii models and implementations

hyunghona/emu

4 papers 13

Datasets

StarCraft II Learning Environment

SMAC-Exp

Lani

MSC

Subtasks

No subtasks available.

Most implemented papers

The StarCraft Multi-Agent Challenge

Tabish Rashid, Mikayel Samvelyan, C. S. D. Witt, Gregory Farquhar, Jakob N. Foerster, Shimon Whiteson, Philip H. S. Torr, Nantas Nardelli, Tim G. J. Rudner, Chia-Man Hung•Sun Feb 10 2019

The StarCraft Multi-Agent Challenge (SMAC), based on the popular real-time strategy game StarCraft II, is proposed as a benchmark problem and an open-source deep multi-agent RL learning framework including state-of-the-art algorithms is opened.

1131

Content

oxwhirl/pymarl

2 papers 1,716

Denys88/rl_games

2 papers 708

inoryy/reaver

2 papers 555

facebookresearch/benchmarl

2 papers 150

puyuan1996/MARL

2 papers 35

0

Paper Graph

QMIX: Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learning

Tabish Rashid, Mikayel Samvelyan, C. S. D. Witt, Gregory Farquhar, Jakob N. Foerster, Shimon Whiteson•Thu Mar 29 2018

QMIX employs a network that estimates joint action-values as a complex non-linear combination of per-agent values that condition only on local observations, and structurally enforce that the joint-action value is monotonic in the per- agent values, which allows tractable maximisation of the jointaction-value in off-policy learning.

1846 0

Paper Graph

The Surprising Effectiveness of PPO in Cooperative Multi-Agent Games

Eugene Vinitsky, A. Bayen, Chao Yu, Akash Velu, Yu Wang, Yi Wu•Mon Mar 01 2021

It is shown that PPO-based multi-agent algorithms achieve surprisingly strong performance in four popularMulti-agent testbeds: the particle-world environments, the StarCraft multi- agent challenge, Google Research Football, and the Hanabi challenge, with minimal hyperparameter tuning and without any domain-specific algorithmic modifications or architectures.

1898 0

Paper Graph

StarCraft II: A New Challenge for Reinforcement Learning

T. Ewalds, O. Vinyals, K. Simonyan, Julian Schrittwieser, Stig Petersen, David Silver, T. Lillicrap, H. V. Hasselt, T. Schaul, Petko Georgiev, John Quan, J. Agapiou, Sergey Bartunov, A. Vezhnevets, Michelle Yeo, Alireza Makhzani, Heinrich Küttler, Stephen Gaffney, Kevin Calderone, Paul Keet, Anthony Brunasso, David Lawrence, Anders Ekermo, J. Repp, Rodney Tsing•Tue Aug 15 2017

This paper introduces SC2LE (StarCraft II Learning Environment), a reinforcement learning environment based on the StarCraft II game that offers a new and challenging environment for exploring deep reinforcement learning algorithms and architectures and gives initial baseline results for neural networks trained from this data to predict game outcomes and player actions.

935 0

Paper Graph

HeR-DRL:Heterogeneous Relational Deep Reinforcement Learning for Single-Robot and Multi-Robot Crowd Navigation

Xinyu Zhou, Songhao Piao, Wenzheng Chi, Liguo Chen, Wei Li•Wed Apr 30 2025

Crowd navigation has garnered significant research attention in recent years, particularly with the advent of DRL-based methods. Current DRL-based methods have extensively explored interaction relationships in single-robot scenarios. However, the heterogeneity of multiple interaction relationships is often disregarded. This “interaction blind spot” hinders progress towards more complex scenarios, such as multi-robot crowd navigation. In this letter, we propose a heterogeneous relational deep reinforcement learning method, named HeR-DRL, which utilizes a customized heterogeneous Graph Neural Network (GNN) to enhance overall performance in crowd navigation. Firstly, we devised a method for constructing robot-crowd heterogenous relation graph that effectively simulates the heterogeneous pair-wise interaction relationships. Based on this graph, we proposed a novel heterogeneous GNN to encode interaction relationship information. Finally, we incorporate the encoded information into deep reinforcement learning to explore the optimal policy. HeR-DRL is rigorously evaluated by comparing it to state-of-the-art algorithms in both single-robot and multi-robot circle crossing scenarios. The experimental results demonstrate that HeR-DRL surpasses the state-of-the-art approaches in overall performance, particularly excelling in terms of efficiency and comfort. This underscores the significance of heterogeneous interactions in crowd navigation.

3 0

Paper Graph

Perceiver IO: A General Architecture for Structured Inputs & Outputs

Jean-Baptiste Alayrac, Sebastian Borgeaud, O. Vinyals, Andrew Zisserman, M. Botvinick, Evan Shelhamer, João Carreira, Carl Doersch, Skanda Koppula, Andrew Jaegle, Catalin Ionescu, David Ding, Andrew Brock, Olivier J. H'enaff•Thu Jul 29 2021

This work proposes Perceiver IO, a general-purpose architecture that handles data from arbitrary settings while scaling linearly with the size of inputs and outputs and augments the Perceiver with a flexible querying mechanism that enables outputs of various sizes and semantics, doing away with the need for task-specific architecture engineering.

739 0

Paper Graph

QPLEX: Duplex Dueling Multi-Agent Q-Learning

Yang Yu, Jianhao Wang, Zhizhou Ren, Terry Liu, Chongjie Zhang•Sun Aug 02 2020

A novel MARL approach, called duPLEX dueling multi-agent Q-learning (QPLEX), which takes a duplex dueling network architecture to factorize the joint value function and encodes the IGM principle into the neural network architecture and thus enables efficient value function learning.

553 0

Paper Graph

FACMAC: Factored Multi-Agent Centralised Policy Gradients

Tabish Rashid, C. S. D. Witt, Shimon Whiteson, Philip H. S. Torr, Bei Peng, Pierre-Alexandre Kamienny, Wendelin Bohmer•Fri Mar 13 2020

FACMAC is a new method for cooperative multi-agent reinforcement learning in both discrete and continuous action spaces that uses a centralised but factored critic, which combines per-agent utilities into the joint action-value function via a non-linear monotonic function.

330 0

Paper Graph

Gym-µRTS: Toward Affordable Full Game Real-time Strategy Games Research with Deep Reinforcement Learning

Chris Bamford, Sheng-Jun Huang, Santiago Ontañón, Lukasz Grela•Fri May 21 2021

Gym-JLRTS (pronounced “gym-micro-RTS”) is introduced as a fast-to-run RL environment for full-game RTS research and a collection of techniques to scale DRL to play full- game µRTS as well as ablation studies to demonstrate their empirical importance.

41 0

Paper Graph

TStarBots: Defeating the Cheating Level Builtin AI in StarCraft II in the Full Game

Ji Liu, T. Zhang, Qing Wang, Jiechao Xiong, Peng Sun, Xinghai Sun, Lei Han, Bo Li, Yang Zheng, Yongsheng Liu, Han Liu•Tue Sep 18 2018

This is the first public work to investigate AI agents that can defeat the built-in AI in the StarCraft II full game, and the AI agent TStarBot1 is based on deep reinforcement learning over a flat action structure and theAI agent T starBot2 isbased on hard-coded rules over a hierarchical action structure.

77 0

Paper Graph

Adding a benchmark result helps the community track progress.