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TABISH RASHID
Browse, search & ask about the research work by "KRIKAMOL MUANDET"
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Selected work | Use "Search" to find all #paper(s): 19
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Paper
The StarCraft Multi-Agent Challenge
IF:8
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: In this paper, we propose the StarCraft Multi-Agent Challenge (SMAC) as a benchmark problem to fill this gap.
MIKAYEL SAMVELYAN
et. al.
arxiv-cs.LG
2019-02-11
Paper
QMIX: Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learning
IF:8
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: We evaluate QMIX on a challenging set of StarCraft II micromanagement tasks, and show that QMIX significantly outperforms existing value-based multi-agent reinforcement learning methods.
TABISH RASHID
et. al.
icml
2018-07-10
Paper
QMIX: Monotonic Value Function Factorisation For Deep Multi-Agent Reinforcement Learning
IF:8
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: We evaluate QMIX on a challenging set of StarCraft II micromanagement tasks, and show that QMIX significantly outperforms existing value-based multi-agent reinforcement learning methods.
TABISH RASHID
et. al.
arxiv-cs.LG
2018-03-30
Paper
Monotonic Value Function Factorisation For Deep Multi-Agent Reinforcement Learning
IF:7
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: To evaluate the performance of QMIX, we propose the StarCraft Multi-Agent Challenge (SMAC) as a new benchmark for deep multi-agent reinforcement learning.
TABISH RASHID
et. al.
arxiv-cs.LG
2020-03-19
Paper
MAVEN: Multi-Agent Variational Exploration
IF:6
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: In this paper, we analyse value-based methods that are known to have superior performance in complex environments [43].
ANUJ MAHAJAN
et. al.
arxiv-cs.LG
2019-10-16
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