First draft for modular Hindsight Experience Replay Transform #2667
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Makes sense to me! Thanks for working on this.
I'd love to see some tests to understand better how this all works.
I left some comments here and there, mostly about formatting and high-level design decisions. Happy to give it a more thorough technical look later once there's an example of how to run it and/or some tests to rely upon.
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maybe let's create a dedicated file for these?
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Give the command on where you would like me to put these and I will do it.
| if len(trajectories.shape) == 1: | ||
| trajectories = trajectories.unsqueeze(0) | ||
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| batch_size, trajectory_len = trajectories.shape |
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maybe
| batch_size, trajectory_len = trajectories.shape | |
| *batch_size, trajectory_len = trajectories.shape |
to account for batch size > 2
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At the moment I assume that we have a single trajectory or a batch of trajectories [b, t]. I am not sure what other cases there may be, but we can think about it.
| def her_augmentation(self, trajectories: TensorDictBase): | ||
| if len(trajectories.shape) == 1: | ||
| trajectories = trajectories.unsqueeze(0) | ||
| batch_size, trajectory_length = trajectories.shape |
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maybe
| batch_size, trajectory_length = trajectories.shape | |
| *batch_size, trajectory_length = trajectories.shape |
| # Create new trajectories | ||
| augmented_trajectories = [] | ||
| list_idxs = [] | ||
| for i in range(batch_size): |
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| for i in range(batch_size): | |
| for i in range(batch_size.numel()): |
which also works with batch_size=torch.Size([])!
| return trajectories | ||
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| class HindsightExperienceReplayTransform(Transform): |
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Don't we need to modify the specs?
Does this work with replay buffer (static data) or only envs? If the latter, we should not be using forward.
If you look at Compose, there are a bunch of things that need to be implemented when nesting transforms, like clone, cache eraser etc.
Perhaps we could inherit from Compose and rewrite forward, _apply_transform, _call, _reset etc such that the logic hold but the extra features are included automatically?
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I think it's a method that we do not need to attach to an environment but it's a data augmentation method. The gist of the augmentation is: Given a trajectory we sample some intermediate states and assume that they are the goal instead. Thus, we can get some positive rewards for hard cases.
Description
I have added the Hindsight Experience Replay Transform specifically implementing the
futureandlaststrategy as described in the paper. The transform is a combination of 3 transforms:Motivation and Context
It's a modular implementation for hindsight experience replay as requested #1819
Types of changes
What types of changes does your code introduce? Remove all that do not apply:
Checklist
Go over all the following points, and put an
xin all the boxes that apply.If you are unsure about any of these, don't hesitate to ask. We are here to help!