Alok Singh fd234e3171 [rllib] Fix A3C PyTorch implementation (#2036) ... * 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		2018-05-30 10:48:11 -07:00
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Dockerfile	[rllib] Fix A3C PyTorch implementation (#2036)	2018-05-30 10:48:11 -07:00