Starcraft II

34 papers with code • 2 benchmarks • 3 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 )

Greatest papers with code

StarCraft II: A New Challenge for Reinforcement Learning

deepmind/pysc2 16 Aug 2017

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

Starcraft Starcraft II

The StarCraft Multi-Agent Challenge

oxwhirl/pymarl 11 Feb 2019

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

SMAC Starcraft +1

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

oxwhirl/pymarl ICML 2018

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.

Multi-agent Reinforcement Learning Starcraft +1

Relational Deep Reinforcement Learning

inoryy/reaver 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.

Relational Reasoning Starcraft +1

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

marlbenchmark/on-policy 2 Mar 2021

Proximal Policy Optimization (PPO) is a popular on-policy reinforcement learning algorithm but is significantly less utilized than off-policy learning algorithms in multi-agent settings.

Starcraft Starcraft II

MSC: A Dataset for Macro-Management in StarCraft II

wuhuikai/MSC 9 Oct 2017

We also split MSC into training, validation and test set for the convenience of evaluation and comparison.

Platform Starcraft +1

Deep RTS: A Game Environment for Deep Reinforcement Learning in Real-Time Strategy Games

cair/DeepRTS 15 Aug 2018

Reinforcement learning (RL) is an area of research that has blossomed tremendously in recent years and has shown remarkable potential for artificial intelligence based opponents in computer games.

Starcraft Starcraft II

FACMAC: Factored Multi-Agent Centralised Policy Gradients

schroederdewitt/multiagent_mujoco 14 Mar 2020

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.

Q-Learning SMAC +2

TStarBot-X: An Open-Sourced and Comprehensive Study for Efficient League Training in StarCraft II Full Game

tencent-ailab/tleague_projpage 27 Nov 2020

We show that with orders of less computation scale, a faithful reimplementation of AlphaStar's methods can not succeed and the proposed techniques are necessary to ensure TStarBot-X's competitive performance.

Imitation Learning Starcraft +1

TLeague: A Framework for Competitive Self-Play based Distributed Multi-Agent Reinforcement Learning

tencent-ailab/tleague_projpage 25 Nov 2020

This poses non-trivial difficulties for researchers or engineers and prevents the application of MARL to a broader range of real-world problems.

Dota 2 Multi-agent Reinforcement Learning +2