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https://github.com/vale981/ray
synced 2025-03-05 18:11:42 -05:00
fd234e3171
* Use F.softmax instead of a pointless network layer Stateless functions should not be network layers. * Use correct pytorch functions * Rename argument name to out_size Matches in_size and makes more sense. * Fix shapes of tensors Advantages and rewards both should be scalars, and therefore a list of them should be 1D. * Fmt * replace deprecated function * rm unnecessary Variable wrapper * rm all use of torch Variables Torch does this for us now. * Ensure that values are flat list * Fix shape error in conv nets * fmt * Fix shape errors Reshaping the action before stepping in the env fixes a few errors. * Add TODO * Use correct filter size Works when `self.config['model']['channel_major'] = True`. * Add missing channel major * Revert reshape of action This should be handled by the agent or at least in a cleaner way that doesn't break existing envs. * Squeeze action * Squeeze actions along first dimension This should deal with some cases such as cartpole where actions are scalars while leaving alone cases where actions are arrays (some robotics tasks). * try adding pytorch tests * typo * fixup docker messages * Fix A3C for some envs Pendulum doesn't work since it's an edge case (expects singleton arrays, which `.squeeze()` collapses to scalars). * fmt * nit flake * small lint
10 lines
414 B
Docker
10 lines
414 B
Docker
# The examples Docker image adds dependencies needed to run the examples
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FROM ray-project/deploy
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# This updates numpy to 1.14 and mutes errors from other libraries
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RUN conda install -y numpy
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RUN apt-get install -y zlib1g-dev
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RUN pip install gym[atari] opencv-python==3.2.0.8 tensorflow
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RUN pip install --upgrade git+git://github.com/hyperopt/hyperopt.git
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RUN conda install pytorch-cpu torchvision-cpu -c pytorch
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