Ok, I figured out why.
I’m training DQN, and there have two network (q and target_q) when training.
in the training process, only update one of the net (q), and the other net (target_q) will periodically copy parameters from the q net.
The performance problem is because I only save the q net parameters, after I save the both net, the issue is solved.