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View Code? Open in Web Editor NEWA Deep Reinforcement Learning Challenge on Forex Portfolio Management
License: GNU General Public License v3.0
A Deep Reinforcement Learning Challenge on Forex Portfolio Management
License: GNU General Public License v3.0
Hi,
Any description of the features used in this work and how to create them?
Cheers
Hi nice work, thanks for sharing.
I am running example_cpu.py and getting lots of errors like the following
Process Process-21:
Traceback (most recent call last):
File "/opt/conda/lib/python3.6/multiprocessing/process.py", line 258, in _bootstrap
self.run()
File "/opt/conda/lib/python3.6/multiprocessing/process.py", line 93, in run
self._target(*self._args, **self._kwargs)
File "example_cpu.py", line 72, in worker
policy_d)
File "example_cpu.py", line 32, in calculate_reward
action = model.forward(state, policy) if torch.is_tensor(policy) else model.forward(state)
File "example_cpu.py", line 144, in forward
if torch.is_tensor(policy): return torch.matmul(state.double(), policy.double())
RuntimeError: size mismatch, m1: [1 x 518], m2: [515 x 4] at /pytorch/aten/src/TH/generic/THTensorMath.cpp:940
the code still runs but I am not sure what results I can get.
Why do we have m1 and m2 with different sizes?
Thanks for sharing, very interesting results. However, I don't quite get what the VAE plots mean. The return (c) is plotted against the first element of the latent vector. I don't see a strong correlation between the two, which indicats the movement is captured?
forex-rl-challenge/autoencoder.py
Lines 94 to 101 in 2ceb64e
Could you create some videos to teach us how it works and how to contribute for this?
I excitend to see this code since I started to learning Dqn and I hope I could test and contribute with something as soon as possible
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