# Starcraft II

56 papers with code • 3 benchmarks • 4 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 )

## Libraries

Use these libraries to find Starcraft II models and implementations
2 papers
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# The StarCraft Multi-Agent Challenge

11 Feb 2019

In this paper, we propose the StarCraft Multi-Agent Challenge (SMAC) as a benchmark problem to fill this gap.

16

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

At the same time, it is often possible to train the agents in a centralised fashion in a simulated or laboratory setting, where global state information is available and communication constraints are lifted.

11

# StarCraft II: A New Challenge for Reinforcement Learning

16 Aug 2017

Finally, we present initial baseline results for canonical deep reinforcement learning agents applied to the StarCraft II domain.

10

# Relational Deep Reinforcement Learning

5 Jun 2018

We introduce an approach for deep reinforcement learning (RL) that improves upon the efficiency, generalization capacity, and interpretability of conventional approaches through structured perception and relational reasoning.

7

# The Surprising Effectiveness of PPO in Cooperative, Multi-Agent Games

2 Mar 2021

This is often due to the belief that PPO is significantly less sample efficient than off-policy methods in multi-agent systems.

7

# Perceiver IO: A General Architecture for Structured Inputs & Outputs

A central goal of machine learning is the development of systems that can solve many problems in as many data domains as possible.

7

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

19 Sep 2018

Both TStarBot1 and TStarBot2 are able to defeat the built-in AI agents from level 1 to level 10 in a full game (1v1 Zerg-vs-Zerg game on the AbyssalReef map), noting that level 8, level 9, and level 10 are cheating agents with unfair advantages such as full vision on the whole map and resource harvest boosting.

3

# FACMAC: Factored Multi-Agent Centralised Policy Gradients

We propose FACtored Multi-Agent Centralised policy gradients (FACMAC), a new method for cooperative multi-agent reinforcement learning in both discrete and continuous action spaces.

3

# QPLEX: Duplex Dueling Multi-Agent Q-Learning

This paper presents 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.

3

# Gym-$μ$RTS: Toward Affordable Full Game Real-time Strategy Games Research with Deep Reinforcement Learning

21 May 2021

In recent years, researchers have achieved great success in applying Deep Reinforcement Learning (DRL) algorithms to Real-time Strategy (RTS) games, creating strong autonomous agents that could defeat professional players in StarCraft~II.

3