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Starcraft II

6 papers with code · Playing Games

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

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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. On the mini-games, these agents learn to achieve a level of play that is comparable to a novice player.

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. Various downstream tasks and analyses of the dataset are also described for the sake of research on macro-management in StarCraft II.

STARCRAFT STARCRAFT II

The StarCraft Multi-Agent Challenge

11 Feb 2019oxwhirl/pymarl

A particularly challenging class of problems in this area is partially observable, cooperative, multi-agent learning, in which teams of agents must learn to coordinate their behaviour while conditioning only on their private observations. In this paper, we propose the StarCraft Multi-Agent Challenge (SMAC) as a benchmark problem to fill this gap.

MULTI-AGENT REINFORCEMENT LEARNING STARCRAFT STARCRAFT II

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

19 Sep 2018Tencent/TStarBots

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. To the best of our knowledge, this is the first public work to investigate AI agents that can defeat the built-in AI in the StarCraft II full game.

DECISION MAKING STARCRAFT STARCRAFT II

Relational Deep Reinforcement Learning

5 Jun 2018nathanin/pad

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. It uses self-attention to iteratively reason about the relations between entities in a scene and to guide a model-free policy.

RELATIONAL REASONING STARCRAFT STARCRAFT II

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

ICML 2018 mawright/attn_rl

In many real-world settings, a team of agents must coordinate their behaviour while acting in a decentralised way. 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