Table 1.

Reward comparison among the proposed DQN method, one-size-fit-all, random forest, and experts’ treatment.

Treatments	Method	Reward	95% Confidence Interval
AGVHD	DQN	0.717	(0.683, 0.729)
	one-size-fit-all	0.693	(0.659, 0.705)
	Random forest	0.677	(0.666, 0.703)
	experts’ treatment	0.673	(0.663, 0.694)
CGVHD	DQN	0.706	(0.678, 0.722)
	one-size-fit-all	0.684	(0.671, 0.712)
	Random forest	0.672	(0.663, 0.713)
	experts’ treatment	0.671	(0.661, 0.697)