Dialogue manager domain adaptation using Gaussian process reinforcement learning
Table 3: Performance of best generic prior when adapted using 50K additional dialogues. Results are given with 95% confidence intervals.
['SFR Name', 'SFR Reward', 'SFR Success', 'SFR #Turns']
The results show that additional in-domain adaptation has the potential to improve the performance further. So when enough training data is available, it is beneficial to create individual in-domain policies rather than continuing to train the generic policy.