vy007vikas / pytorch-actorcriticrl Goto Github PK

I am writing to thank you for what you write in the README, which helps me totally understand the idea of DDPG. Thanks a lot

Traceback (most recent call last)
36 state = np.float32(observation)
37
---> 38 action = trainer.get_exploration_action(state)
39 # if _ep%5 == 0:
40 # # validate every 5th episode

.../train.py in get_exploration_action(self, state)
62 """
63 state = Variable(torch.from_numpy(state))
---> 64 action = self.actor.forward(state).detach()
65 new_action = action.data.numpy() + (self.noise.sample() * self.action_lim)
66 return new_action

.../model.py in forward(self, state)
97 action = torch.tanh(self.fc4(x))
98
---> 99 action = action * self.action_lim
100
101 return action

TypeError: mul() received an invalid combination of arguments - got (numpy.int64), but expected one of:

• (Tensor other)
  didn't match because some of the arguments have invalid types: (numpy.int64)
• (Number other)
  didn't match because some of the arguments have invalid types: (numpy.int64)

Any suggestions with this error: ?

Traceback (most recent call last):
File "main.py", line 72, in
trainer.save_models(_ep)
File "/home/sayomakinwa/.mujoco2/mujoco-py/PyTorch-ActorCriticRL/train.py", line 115, in save_models
torch.save(self.target_actor.state_dict(), './Models/' + str(episode_count) + '_actor.pt')
File "/home/sayomakinwa/anaconda3/lib/python3.7/site-packages/torch/serialization.py", line 219, in save
return _with_file_like(f, "wb", lambda f: _save(obj, f, pickle_module, pickle_protocol))
File "/home/sayomakinwa/anaconda3/lib/python3.7/site-packages/torch/serialization.py", line 142, in _with_file_like
f = open(f, mode)
FileNotFoundError: [Errno 2] No such file or directory: './Models/0_actor.pt'

in train.py

loss_critic = F.smooth_l1_loss(y_predicted, y_expected)
self.critic_optimizer.zero_grad()
loss_critic.backward()
self.critic_optimizer.step()

Is it correct to set the gradients in the optimizer to zero after calculating the loss or should it be the other way around i.e. setting all gradients to zero, then taking the loss and performing an update?