43 lines
2.1 KiB
Python
43 lines
2.1 KiB
Python
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# License: see [LICENSE, LICENSES/rsl_rl/LICENSE]
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import torch
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def split_and_pad_trajectories(tensor, dones):
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""" Splits trajectories at done indices. Then concatenates them and padds with zeros up to the length og the longest trajectory.
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Returns masks corresponding to valid parts of the trajectories
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Example:
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Input: [ [a1, a2, a3, a4 | a5, a6],
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[b1, b2 | b3, b4, b5 | b6]
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f]
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Output:[ [a1, a2, a3, a4], | [ [True, True, True, True],
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[a5, a6, 0, 0], | [True, True, False, False],
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[b1, b2, 0, 0], | [True, True, False, False],
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[b3, b4, b5, 0], | [True, True, True, False],
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[b6, 0, 0, 0] | [True, False, False, False],
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] | ]
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Assumes that the inputy has the following dimension order: [time, number of envs, aditional dimensions]
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"""
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dones = dones.clone()
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dones[-1] = 1
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# Permute the buffers to have order (num_envs, num_transitions_per_env, ...), for correct reshaping
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flat_dones = dones.transpose(1, 0).reshape(-1, 1)
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# Get length of trajectory by counting the number of successive not done elements
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done_indices = torch.cat((flat_dones.new_tensor([-1], dtype=torch.int64), flat_dones.nonzero()[:, 0]))
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trajectory_lengths = done_indices[1:] - done_indices[:-1]
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trajectory_lengths_list = trajectory_lengths.tolist()
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# Extract the individual trajectories
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trajectories = torch.split(tensor.transpose(1, 0).flatten(0, 1),trajectory_lengths_list)
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padded_trajectories = torch.nn.utils.rnn.pad_sequence(trajectories)
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trajectory_masks = trajectory_lengths > torch.arange(0, tensor.shape[0], device=tensor.device).unsqueeze(1)
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return padded_trajectories, trajectory_masks
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def unpad_trajectories(trajectories, masks):
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""" Does the inverse operation of split_and_pad_trajectories()
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"""
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# Need to transpose before and after the masking to have proper reshaping
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return trajectories.transpose(1, 0)[masks.transpose(1, 0)].view(-1, trajectories.shape[0], trajectories.shape[-1]).transpose(1, 0)
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