Multi-Agent Task-Oriented Dialog Policy Learning with Role-Aware Reward Decomposition
Table 3: Performance of the interaction between the benchmark user policy and each system agent.
['System', 'Turns', 'Inform', 'Match', 'Success']
The SOTA performance from GDPL Takanobu et al. Among all the methods, MADPL has achieved the highest task success and the second-highest match rate. All the methods experience a decline in inform F1 after the RL training. Fig. This is because the action space of the system policy is much larger, thus more challenging to learn. In spite of that, the success rate of MADPL shows a rising trend.