https://github.com/RL-WFU/IC3Net-Highway
Learning when to Communicate at Scale in Multiagent Cooperative and Competitive Tasks
Papers with Code 아카이브
Real-Time Strategy Games
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Real-Time Strategy (RTS) tasks involve training an agent to play video games with continuous gameplay and high-level macro-strategic goals such as map control, economic superiority and more.
<span style="color:grey; opacity: 0.6">( Image credit: Multi-platform Version of StarCraft: Brood War in a Docker Container )</span>
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/SforAiDl/vformer
Perceiver IO: A General Architecture for Structured Inputs & Outputs
https://github.com/Stable-Baselines-Team/stable-baselines3-contrib
A Closer Look at Invalid Action Masking in Policy Gradient Algorithms
https://github.com/TeamSAIDA/SAIDA
https://github.com/Teslatic/SC2-Freiburg
https://github.com/TestGenome/MSCS
https://github.com/TonghanWang/DOP
QMIX: Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learning
https://github.com/TonghanWang/RODE
https://github.com/TorchCraft/TorchCraft
TorchCraft: a Library for Machine Learning Research on Real-Time Strategy Games
https://github.com/ailabdsunipi/pymarlzooplus
https://github.com/anonymous-iclr22/trust-region-in-multi-agent-reinforcement-learning
Is Independent Learning All You Need in the StarCraft Multi-Agent Challenge?
https://github.com/anonymous1234517/code
Stabilising Experience Replay for Deep Multi-Agent Reinforcement Learning
https://github.com/apsdehal/gym-starcraft
Learning when to Communicate at Scale in Multiagent Cooperative and Competitive Tasks
https://github.com/asmerdov/DataCollectionSystem
https://github.com/bewrrrie/ChessAI
Experiments with Game Tree Search in Real-Time Strategy Games
https://github.com/chandar-lab/coe
Episodic Multi-agent Reinforcement Learning with Curiosity-Driven Exploration
https://github.com/chauncygu/multi-agent-constrained-policy-optimisation
Is Independent Learning All You Need in the StarCraft Multi-Agent Challenge?
https://github.com/code-cultivater/MAPPG
https://github.com/cts198859/deeprl_dist
Stabilising Experience Replay for Deep Multi-Agent Reinforcement Learning
https://github.com/cts198859/deeprl_network
Stabilising Experience Replay for Deep Multi-Agent Reinforcement Learning
https://github.com/cyanrain7/trpo-in-marl
Is Independent Learning All You Need in the StarCraft Multi-Agent Challenge?
https://github.com/cyanrain7/trust-region-policy-optimisation-in-multi-agent-reinforcement-learning
Is Independent Learning All You Need in the StarCraft Multi-Agent Challenge?
https://github.com/deepmind/alphastar
AlphaStar Unplugged: Large-Scale Offline Reinforcement Learning
https://github.com/dongchen06/macacc
Stabilising Experience Replay for Deep Multi-Agent Reinforcement Learning
https://github.com/dtabas/epymarl
https://github.com/emerge-lab/nocturne_lab
The Surprising Effectiveness of PPO in Cooperative, Multi-Agent Games
https://github.com/ericborn/binarybot
https://github.com/esceptico/perceiver-io
Perceiver IO: A General Architecture for Structured Inputs & Outputs
https://github.com/facebookarchive/MazeBase
https://github.com/facebookresearch/benchmarl
QMIX: Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learning
https://github.com/farama-foundation/microrts-py
Action Guidance: Getting the Best of Sparse Rewards and Shaped Rewards for Real-time Strategy Games
https://github.com/gingkg/smac
https://github.com/goodbyeearth/myVBC
Efficient Communication in Multi-Agent Reinforcement Learning via Variance Based Control
https://github.com/google-deepmind/pysc2
https://github.com/hcch0912/gym_wrapper_dm_soccer
https://github.com/hhhusiyi-monash/UPDeT
QMIX: Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learning
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.