Comments (1)
Hi. TorchRL and Pearl have been developed independently inside Meta. TorchRL is more integrated with the PyTorch environment, and has been around for longer than Pearl. Pearl has been developed with a target of being increasingly better integrated with production systems, and to provide easy customization for things like history summarization and safety modules. However, TorchRL can certainly also be used in production cases.
I don't think there is a clear cut answer as to when to use one or the other. I would advise you to look at the tutorials for one and the other and see which one has a style that agrees more with your preferences and use cases.
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Related Issues (20)
- Frozen-lake tutorial HOT 1
- Question: train_via_uniform_data HOT 10
- Unable to export loss values from TD3 HOT 12
- cuda out of meomory HOT 16
- Simplify actor-critic with shared layers HOT 2
- save the trained agent for reuse HOT 2
- custom network issue HOT 1
- Pip availability and clear versioning in branches/tags/releases HOT 2
- PPO on continuous actions HOT 9
- Error when running the Single Item Recommender System Notebook HOT 5
- How is Pearl different from TorchRL? HOT 1
- Creating a project roadmap HOT 1
- Bug in tutorial single item recommender system: can't instantiate abstract class RecEnv HOT 3
- Support for imitation learning HOT 3
- RuntimeError for action shape HOT 3
- What is the use of the attribute 'state' in the RecEnv from the single item recommender tutorial? HOT 1
- Rainbow DQN HOT 1
- Add voice message capabilities HOT 1
- How exactly is Pearl "Production-ready"? HOT 1
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