Timodhau
Timodhau
Well i took a look just before : `with _torch.no_grad(): for i in range(environment.max_step): s_tensor = _torch.as_tensor((state,), device=device) a_tensor = act(s_tensor) # action_tanh = act.forward() action = ( a_tensor.detach().cpu().numpy()[0] )...