Comments (3)
This is currently not supported in the code, but you could try hacking the code easily to try it out. The problem with such a pipeline might be that it involves too many moving parts and it might increase your experimental life-cycle. When compared to the complexities introduced by having such a pipeline, I also don't see a big advantage to doing this when compared to joint training. Right now, it converges reasonably quickly (within 3M frames).
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Closing this due to lack of activity. Please feel free to open it if you have further questions.
from occupancyanticipation.
This is currently not supported in the code, but you could try hacking the code easily to try it out. The problem with such a pipeline might be that it involves too many moving parts and it might increase your experimental life-cycle. When compared to the complexities introduced by having such a pipeline, I also don't see a big advantage to doing this when compared to joint training. Right now, it converges reasonably quickly (within 3M frames).
Thanks very much for your explanation!
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Related Issues (20)
- How to ensure that when using the same experimental setup, the model results after two runs are consistent? HOT 2
- actuation noise HOT 4
- Mapper training during policy training HOT 2
- Not able to load trained models HOT 9
- How is "done" variable being defined in exploration task? HOT 1
- What's does height_thresh means when generating map? HOT 2
- RuntimeError when running run.py for training HOT 1
- How to set appropriate values in .yaml file to avoid low GPU-uti but high GPU Memory-usage? HOT 3
- Normalization of image in Mapper HOT 2
- How to generate episode datasets for exploration task after changing the height and radius of agent? HOT 2
- No module named 'habitat_baselines.common.env_utils' HOT 3
- Custom Dataset Evaluation HOT 3
- Run OccupancyAnticipation in Colab HOT 1
- What's the behind meaning of "GT_TYPE: wall_occupancy" in the configuration .yaml file? HOT 2
- Unexpected results for pre-trained model HOT 1
- RuntimeError: inverse_cuda: For batch 0: U(163642880,163642880) is zero, singular U HOT 1
- mp3d configs HOT 2
- RuntimeError: Error(s) in loading state_dict for Mapper: HOT 1
- What does map_size mean? HOT 1
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