gmc-drl / metabox Goto Github PK

Hi, I tried to run the l2l code with:
python main.py --train --problem bbob --difficulty easy --train_agent L2L_Agent --train_optimizer L2L_Optimizer --max_learning_step 150000
but it returned error:

start training: 20240315T134833_bbob_easy_10D
Training L2L_Agent Epoch 0: 0%| | 0/18 [00:00<?, ?it/s]
Traceback (most recent call last):
File "D:\git-repos\MetaBox\src\main.py", line 24, in
trainer.train()
File "D:\git-repos\MetaBox\src\trainer.py", line 151, in train
exceed_max_ls, pbar_info_train = self.agent.train_episode(env) # pbar_info -> dict
File "D:\git-repos\MetaBox\src\agent\l2l_agent.py", line 49, in train_episode
y,,=env.step(x)
File "D:\git-repos\MetaBox\src\environment\basic_environment.py", line 22, in step
return self.optimizer.update(action, self.problem)
File "D:\git-repos\MetaBox\src\optimizer\l2l_optimizer.py", line 41, in update
y=problem.eval(x)-problem.optimum
File "D:\git-repos\MetaBox\src\problem\basic_problem.py", line 21, in eval
x = np.array(x)
File "C:\Users\minhh\miniconda3\envs\py39\lib\site-packages\torch_tensor.py", line 643, in array
return self.numpy()
RuntimeError: Can't call numpy() on Tensor that requires grad. Use tensor.detach().numpy() instead.

The line number might differ a little bit since I added some comment code. According to the suggestion, I replace x = np.array(x) by x = x.detach().numpy() but it returned another error:

start training: 20240315T135459_bbob_easy_10D
Training L2L_Agent Epoch 0: 0%| | 0/18 [00:00<?, ?it/s]
Traceback (most recent call last):
File "D:\git-repos\MetaBox\src\main.py", line 24, in
trainer.train()
File "D:\git-repos\MetaBox\src\trainer.py", line 151, in train
exceed_max_ls, pbar_info_train = self.agent.train_episode(env) # pbar_info -> dict
File "D:\git-repos\MetaBox\src\agent\l2l_agent.py", line 65, in train_episode
loss.mean().backward()
AttributeError: 'numpy.float64' object has no attribute 'backward'

What did I do wrong here?