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@Lucaweihs Lucaweihs released this 24 Mar 19:12
· 143 commits to main since this release
v0.5.0
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Learn about vigilant mode.

In this release we add several new substantial features.

Multi-machine distributed training support

We've added a new tutorial (see here) and scripts necessary to be able to run allenact across multiple machines.

Improved Navigation Models with Auxiliary Tasks

Recent work has shown that certain auxiliary tasks (e.g. inverse/forward dynamics) can be used to speed up training and improve the performance of navigation agents. We have implemented a large number of these auxiliary tasks (see for instance the InverseDynamicsLoss, TemporalDistanceLoss, CPCA16Loss, and MultiAuxTaskNegEntropyLoss classes in the allenact.embodiedai.aux_losses.losses module as well as a new base architecture for visual navigation (allenact.embodiedai.models.VisualNavActorCritic) which makes it very easy to use these auxiliary losses during training.

CLIP Preprocessors and Embodied CLIP experiments

We've added a new clip_plugin that makes preprocessors available which use the CLIP-pretrained visual encoders. See the projects/objectnav_baselines/experiments/robothor/clip/objectnav_robothor_rgb_clipresnet50gru_ddppo.py experiment configuration file which uses these new proprocessors to obtain SOTA results on the RoboTHOR ObjectNav leaderboard. These results correspond to our new paper on using CLIP visual encoders for embodied tasks.

New storage flexibility

We've substantially generalized the rollout storage class. This is an "advanced" option but it is now possible to implement custom storage classes which can enable new types of training (e.g. Q-learning) and even mixing various training paradigms (e.g. training with Q-learning, PPO, and offline imitation learning simultaneously).

Better Habitat Support and Experiments

We've added support for training ObjectNav models in Habitat and include a experiment config that trains such a model with a CLIP visual encoder backbone (see projects/objectnav_baselines/experiments/habitat/clip/objectnav_habitat_rgb_clipresnet50gru_ddppo.py).

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