Starcraft
129 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 )
Benchmarks
These leaderboards are used to track progress in Starcraft
Libraries
Use these libraries to find Starcraft models and implementations
VizDoom
StarCraft II Learning Environment
Mario AI
Lani
StarData
MSC
Latest papers
Offline Multi-Agent Reinforcement Learning with Implicit Global-to-Local Value Regularization
zhengyinan-air/omiga
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NeurIPS 2023
Offline reinforcement learning (RL) has received considerable attention in recent years due to its attractive capability of learning policies from offline datasets without environmental interactions.
Maximum Entropy Heterogeneous-Agent Reinforcement Learning
pku-marl/harl
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We embed cooperative MARL problems into probabilistic graphical models, from which we derive the maximum entropy (MaxEnt) objective for MARL.
Semantic HELM: A Human-Readable Memory for Reinforcement Learning
Then we feed these tokens to a pretrained language model that serves the agent as memory and provides it with a coherent and human-readable representation of the past.
A Unified Framework for Factorizing Distributional Value Functions for Multi-Agent Reinforcement Learning
In fully cooperative multi-agent reinforcement learning (MARL) settings, environments are highly stochastic due to the partial observability of each agent and the continuously changing policies of other agents.
EXPODE: EXploiting POlicy Discrepancy for Efficient Exploration in Multi-agent Reinforcement Learning
ZYC9894/EXPODE-master
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International Conference on Autonomous Agents and Multiagent Systems 2023
Recently, Multi-Agent Reinforcement Learning (MARL) has been applied to a large number of scenarios and has shown promising performance.
Is Centralized Training with Decentralized Execution Framework Centralized Enough for MARL?
zyh1999/cadp
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Despite the encouraging results achieved, CTDE makes an independence assumption on agent policies, which limits agents to adopt global cooperative information from each other during centralized training.
SMAClite: A Lightweight Environment for Multi-Agent Reinforcement Learning
The Starcraft Multi-Agent Challenge (SMAC) has been widely used in MARL research, but is built on top of a heavy, closed-source computer game, StarCraft II.
Effective and Stable Role-Based Multi-Agent Collaboration by Structural Information Principles
Role-based learning is a promising approach to improving the performance of Multi-Agent Reinforcement Learning (MARL).
MAC-PO: Multi-Agent Experience Replay via Collective Priority Optimization
ysmei97/mac-po
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To this end, we propose MAC-PO, which formulates optimal prioritized experience replay for multi-agent problems as a regret minimization over the sampling weights of transitions.
Attacking Cooperative Multi-Agent Reinforcement Learning by Adversarial Minority Influence
dig-beihang/ami
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To achieve maximum deviation in victim policies under complex agent-wise interactions, our unilateral attack aims to characterize and maximize the impact of the adversary on the victims.
