lightning-bolts
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| lightning-bolts | PanelCleaner | |
|---|---|---|
| 3 | 1 | |
| 1,644 | 143 | |
| 1.4% | - | |
| 7.5 | 9.6 | |
| 22 days ago | 7 days ago | |
| Python | Python | |
| Apache License 2.0 | GNU General Public License v3.0 only |
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lightning-bolts
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Question about implementing RL algorithms
In the lightning-bolts repository, they implement the different RL algorithms, such as PPO and DQN, as different models. Would it make more sense to have the different algorithms be the Trainer instead? Inside each of the implementations, the model creates the same neural network with different training steps.
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[P] An elegant and strong PyTorch Trainer
This is also how lightning bolts tend to be defined: for example, you have https://github.com/Lightning-AI/lightning-bolts/blob/master/pl_bolts/models/rl/advantage_actor_critic_model.py for A3C, which itself is only the loss and training wrapper around the normal modules for critic and actor (see https://github.com/Lightning-AI/lightning-bolts/blob/52e4c503c671f4866339c1537cf6ae506e7c5cf5/pl_bolts/models/rl/common/networks.py#L147=)
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[D] How to organize deep learning projects on Github ?
Also PyTorch Lighting gives example in this repo https://github.com/PyTorchLightning/pytorch-lightning-bolts
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