Dialogue manager domain adaptation using Gaussian process reinforcement learning
Table 5: Selection of committee members for multi-policy Bayesian committee machine for SFR domain. The committee policy is trained on 7500 dialogues equally spread across three domains.
['MBCM – SFR Committee', 'MBCM – SFR Reward', 'MBCM – SFR Success', 'MBCM – SFR #Turns']
As can be seen from the results, all committee members contribute to performance gains. However not all committee members are equally important. In this case, for good performance on the SFR domain, the SFH committee member is more useful than the L11 committee member.