Question the training process get stuck

The training progress is normal and correct at the begining. The training is fixed and stuck without errors when the step arrives at a number that is not the max step. Most of the time, the other trainings with different hyperparameters and models don't have the problem. I try to use "--resume" but it seems the training doesn't progress, the exe env appears and works but stop moving and fail to respond after a moment. This may have the relation with hyperparameter setting. This time I use PPO and SAC at the same time. What's more, I change the sac buffer size and get the same result. the following picture appears stuck when I use "ctrl c" to stop the training and it have to wait a long time to end the training process.

 

The information about ML
ml-agents: 0.16.1,
ml-agents-envs: 0.16.1,
Communicator API: 1.0.0,
TensorFlow: 1.13.1

 

I have tried to use --initialize-from the stuck model.it trains correctly and properly! I don't know what's the problem.I can't use --resume to retrain it.But i't important for me to keep the curve graph with "--resume"

 

I use debug to see the result process. it turns out that the sac is updating all the time

 

Thank you for your attention! It may end up updating the sac after many hours. But this may waste too much time. The time of the "--resume" can be as much as a new sac training!

 

Sorry, I'm late! Yes, I use --resume with the same run-id. the configuration yaml can be the follwing!
default:
trainer: sac
batch_size: 128
buffer_size: 50000
buffer_init_steps: 0
hidden_units: 128
init_entcoef: 1.0
learning_rate: 3.0e-4
learning_rate_schedule: constant
max_steps: 5.0e5
memory_size: 128
normalize: false
steps_per_update: 10
num_layers: 2
time_horizon: 64
sequence_length: 64
summary_freq: 10000
tau: 0.005
use_recurrent: false
vis_encode_type: simple
reward_signals:
extrinsic:
strength: 1.0
gamma: 0.99

Police_cach:
summary_freq: 30000
time_horizon: 256
batch_size: 256
buffer_init_steps: 10000
buffer_size: 500000
hidden_units: 256
num_layers: 2
init_entcoef: 0.01
max_steps: 1.0e8
sequence_length: 16
tau: 0.01
use_recurrent: false
reward_signals:
extrinsic:
strength: 2.0
gamma: 0.99

 

What's more, the SAC is indeed updating after --resume but it costs too much time! it seems the bug has been fixed in release 3.0 ! But I try to use the ML-Agents Release 3 but it may also need waiting long time! I don't know whether the bug is fixed completely or I use it wrongly.