Comments (4)
I would suggest using an environment wrapper for that. At the moment tianshou is primarily an algorithm library, not focused on wrappers. In fact, you could just use the wrapper from SB3 together with tianshou.
Let me know if this answers your question
from tianshou.
Hi,
Thanks for your reply, and I will try your solution.
But, if I do not use the wrapper from SB3, are there any other ways to record the rewards from all vector environments?
from tianshou.
The best way would be to use an env wrapper. Note that in all examples you can create your own env factory with your own wrapper. I'll try to add a tutorial on how to do that soon.
Apart from that, you can probably use a custom logger. You can also access the buffer directly during training through the trainer, all rewards are saved there.
In very near future we will add support for callbacks during training, which then would provide the simplest way for saving custom data (see #977 #895)
from tianshou.
The best way would be to use an env wrapper. Note that in all examples you can create your own env factory with your own wrapper. I'll try to add a tutorial on how to do that soon.
Apart from that, you can probably use a custom logger. You can also access the buffer directly during training through the trainer, all rewards are saved there.
In very near future we will add support for callbacks during training, which then would provide the simplest way for saving custom data (see #977 #895)
Thanks for your brilliant work!
from tianshou.
Related Issues (20)
- will add dreamerv3 ? HOT 1
- Documentation for multi-agent needs fixing HOT 1
- No minibatch for computation of logp_old in PPOPolicy HOT 1
- MPO Implementation HOT 1
- Improve interface of BasePolicy.compute_action HOT 1
- Suggestion - Redesign RayEnvWorker for Improved Performance HOT 12
- tianshou v1.0.0 failed to install on python 3.12.4 HOT 2
- How to add a obs with multiple dimensions to the buffer?
- TypeError: isinstance() argument 2 cannot be a parameterized generic HOT 1
- Make Batch.__eq__ work also for scalar values (0-dimensional arrays) HOT 2
- EOFError on training HOT 8
- ModuleNotFoundError: No module named 'tianshou.highlevel' HOT 3
- Cannot unify float64 and array(float64, 1d, C) for 'gae.2'
- Discrete distributions are broken HOT 2
- Actor and distribution function should not be specified independently HOT 1
- Learning rate scheduler confusion HOT 1
- Handling LR scheduling when episodes instead of steps are collected
- Release 1.1.0 to pypi HOT 1
- Refactoring: make Trainer more flexible
- Support custom scores for Trainer in high-level interfaces
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