fix and validate reinforce.py works in gpu mode

[jtoy]

i think the new RNN functionality kills it:
https://discuss.pytorch.org/t/training-rnn-on-gpu/3574/5

base_linkepisode: 48

1 touches in current episode <<
env.class_label: 9
Traceback (most recent call last):
File "reinforce.py", line 243, in
output = cnn_lstm(observed_touches) # Prediction
File "/home/jtoy/anaconda3/lib/python3.6/site-packages/torch/nn/modules/module.py", line 206, in call
result = self.forward(*input, **kwargs)
File "reinforce.py", line 146, in forward
rnn_out, rnn_hidden = self.rnn(inp.view(1, 1, -1), rnn_hidden)
File "/home/jtoy/anaconda3/lib/python3.6/site-packages/torch/nn/modules/module.py", line 206, in call
result = self.forward(*input, **kwargs)
File "/home/jtoy/anaconda3/lib/python3.6/site-packages/torch/nn/modules/rnn.py", line 91, in forward
output, hidden = func(input, self.all_weights, hx)
File "/home/jtoy/anaconda3/lib/python3.6/site-packages/torch/nn/_functions/rnn.py", line 343, in forward
return func(input, *fargs, **fkwargs)
File "/home/jtoy/anaconda3/lib/python3.6/site-packages/torch/autograd/function.py", line 202, in _do_forward
flat_output = super(NestedIOFunction, self)._do_forward(*flat_input)
File "/home/jtoy/anaconda3/lib/python3.6/site-packages/torch/autograd/function.py", line 224, in forward
result = self.forward_extended(*nested_tensors)
File "/home/jtoy/anaconda3/lib/python3.6/site-packages/torch/nn/_functions/rnn.py", line 285, in forward_extended
cudnn.rnn.forward(self, input, hx, weight, output, hy)
File "/home/jtoy/anaconda3/lib/python3.6/site-packages/torch/backends/cudnn/rnn.py", line 239, in forward
fn.hx_desc = cudnn.descriptor(hx)
File "/home/jtoy/anaconda3/lib/python3.6/site-packages/torch/backends/cudnn/init.py", line 304, in descriptor
descriptor.set(tensor)
File "/home/jtoy/anaconda3/lib/python3.6/site-packages/torch/backends/cudnn/init.py", line 110, in set
self, _typemap[tensor.type()], tensor.dim(),
KeyError: 'torch.FloatTensor'

[jtoy]

if i comment out the rnn training, then gpu mode works ,so i think it is the issue in the above pytorch link