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arXiv · 2007.12306

Value-Decomposition Multi-Agent Actor-Critics

Jianyu Su·Stephen Adams·Peter A. Beling
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Abstract

The exploitation of extra state information has been an active research area in multi-agent reinforcement learning (MARL). QMIX represents the joint action-value using a non-negative function approximator and achieves the best performance, by far, on multi-agent benchmarks, StarCraft II micromanagement tasks. However, our experiments show that, in some cases, QMIX is incompatible with A2C, a training paradigm that promotes algorithm training efficiency. To obtain a reasonable trade-off between training efficiency and algorithm performance, we extend value-decomposition to actor-critics that are compatible with A2C and propose a novel actor-critic framework, value-decomposition actor-critics (VDACs). We evaluate VDACs on the testbed of StarCraft II micromanagement tasks and demonstrate that the proposed framework improves median performance over other actor-critic methods. Furthermore, we use a set of ablation experiments to identify the key factors that contribute to the performance of VDACs.

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Jianyu Su, Stephen Adams, Peter A. Beling. 2020-07-24. Value-Decomposition Multi-Agent Actor-Critics. https://arxiv.org/abs/2007.12306

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