Starcraft

110 papers with code • 0 benchmarks • 6 datasets

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

( Image credit: Macro Action Selection with Deep Reinforcement Learning in StarCraft )

Libraries

Use these libraries to find Starcraft models and implementations
3 papers
1,466
3 papers
24
2 papers
543
2 papers
452
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Most implemented papers

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.

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.

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.

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

marlbenchmark/on-policy 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.

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.

Perceiver IO: A General Architecture for Structured Inputs & Outputs

deepmind/deepmind-research ICLR 2022

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

Counterfactual Multi-Agent Policy Gradients

opendilab/DI-engine 24 May 2017

COMA uses a centralised critic to estimate the Q-function and decentralised actors to optimise the agents' policies.

Stabilising Experience Replay for Deep Multi-Agent Reinforcement Learning

cts198859/deeprl_dist ICML 2017

Many real-world problems, such as network packet routing and urban traffic control, are naturally modeled as multi-agent reinforcement learning (RL) problems.

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

oxwhirl/wqmix NeurIPS 2020

We show in particular that this projection can fail to recover the optimal policy even with access to $Q^*$, which primarily stems from the equal weighting placed on each joint action.

QPLEX: Duplex Dueling Multi-Agent Q-Learning

wjh720/QPLEX ICLR 2021

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.