ikostrikov / ikostrikov/implicit_q_learning
Why use a positive learning rate in finetuning?
Open
- Dominant language
- Python
- Stars
- 337
- Forks
- 49
- PR merge metrics
- No merged PRs in 30d
Description
In the file train_finetune, this code schedule_fn = optax.cosine_decay_schedule(-actor_lr, max_steps) seems to use a positive learning rate?why?
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Research direction
Start with the train_finetune file and inspect the schedule_fn assignment using optax.cosine_decay_schedule. Clarify in the issue or relevant documentation why the learning-rate value is passed with its shown sign, and consider the question resolved when that behavior is explicitly explained.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- python
- Domain
- machine-learning
- Issue type
- Documentation
- Difficulty
- 1/5
- Estimated time
- Under an hour
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 30/100