A Collaborative Multi-agent Reinforcement Learning Framework for Dialog Action Decomposition (2021.emnlp-main)
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| Challenge: | Currently, most reinforcement learning methods for dialog policy learning train a centralized agent that selects a predefined joint action concatenating domain name, intent type, and slot name. |
| Approach: | They propose a hierarchical multi-agent framework in which each part of the action is led by a different agent and a joint optimization process that makes agents can exchange their policy information. |
| Outcome: | The proposed framework reduces labor costs for action templates and decreases the size of the action space for each agent. |
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