How to train DDPG episode reward more better?

I'm training a DDPG agent from the Reinforcement Learning toolbox. But as you can see, my episode reward never change. I try so many way to fix this problem. Like change the netwoek, Gradient Threshold, Learning Rate. But the result will be the same. I check my reward funtion, if the situation is eligible I will give it some reward or penalty. But its reward is always be same.

Is my condtion have some problem? Or my results are not input into the model? I dont have anyway to do.

[Emmanouil Tzorakoleftherakis]

How did you set the IsDone flag? This may lead to premature episode termination

[Guoge Tan]

Hi, sorry to bother you, but I'd like to ask if your problem is solved or not? I‘m working on a path planning problem using the Reinforcement Learning toolbox on MATLAB R2020a and I also encountered a problem similar to yours.