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Starcraft II is a RTS game; the task is to train an agent to play the game.

( Image credit: The StarCraft Multi-Agent Challenge )

Benchmarks

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Datasets

Greatest papers with code

StarCraft II: A New Challenge for Reinforcement Learning

16 Aug 2017deepmind/pysc2

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

11 Feb 2019oxwhirl/pymarl

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

SMAC STARCRAFT STARCRAFT II

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

ICML 2018 oxwhirl/pymarl

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 STARCRAFT II

Relational Deep Reinforcement Learning

5 Jun 2018inoryy/reaver

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 STARCRAFT II

MSC: A Dataset for Macro-Management in StarCraft II

9 Oct 2017wuhuikai/MSC

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

STARCRAFT STARCRAFT II

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

15 Aug 2018cair/DeepRTS

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

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

2 Mar 2021marlbenchmark/on-policy

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 problems.

STARCRAFT STARCRAFT II

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

27 Nov 2020tencent-ailab/tleague_projpage

In this paper, we introduce a new AI agent, named TStarBot-X, that is trained under limited computation resources and can play competitively with expert human players.

IMITATION LEARNING STARCRAFT STARCRAFT II

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

25 Nov 2020tencent-ailab/tleague_projpage

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 STARCRAFT STARCRAFT II

Deep Multi-Agent Reinforcement Learning for Decentralized Continuous Cooperative Control

14 Mar 2020schroederdewitt/multiagent_mujoco

To demonstrate the utility of MAMuJoCo, we present a range of benchmark results on this new suite, including comparing the state-of-the-art actor-critic method MADDPG against two novel variants of existing methods.

Q-LEARNING SMAC STARCRAFT STARCRAFT II