Multi-Agent Task-Oriented Dialog Policy Learning with Role-Aware Reward Decomposition
Table 4: Performance of the interaction between each user agent and the benchmark system policy.
['User', 'Turns', 'Inform', 'Match', 'Success']
Among all the methods, MADPL has achieved the highest inform F1 and task success. Though CRL improves the performance at the beginning, the success rate fails to increase further afterwards, while MADPL continues to improve all the time. This also indirectly indicates the advantage of using role-aware reward decomposition in HVN.