Explaining sequences of actions in multi-agent deep reinforcement learning models
This paper introduces a method to explain MADRL agents’ behaviors by abstracting their actions into high-level strategies. Particularly, a spatio-temporal neural network model is applied to encode the agents’ sequences of actions as memory episodes wherein an aggregating memory retrieval can general...
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| Main Authors: | , , , , |
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| Format: | text |
| Language: | English |
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Institutional Knowledge at Singapore Management University
2024
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| Subjects: | |
| Online Access: | https://ink.library.smu.edu.sg/sis_research/9783 https://ink.library.smu.edu.sg/context/sis_research/article/10783/viewcontent/p2537__1_.pdf |
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| Institution: | Singapore Management University |
| Language: | English |
| Summary: | This paper introduces a method to explain MADRL agents’ behaviors by abstracting their actions into high-level strategies. Particularly, a spatio-temporal neural network model is applied to encode the agents’ sequences of actions as memory episodes wherein an aggregating memory retrieval can generalize them into a concise abstract representation of collective strategies. To assess the effectiveness of our method, we applied it to explain the actions of QMIX MADRL agents playing a StarCraft Multi-agent Challenge (SMAC) video game. A user study on the perceived explainability of the extracted strategies indicates that our method can provide comprehensible explanations at various levels of granularity. |
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