10d919052b
The new proposed feature is to have trainers as generators.
The usage pattern is:
```python
trainer = OnPolicyTrainer(...)
for epoch, epoch_stat, info in trainer:
print(f"Epoch: {epoch}")
print(epoch_stat)
print(info)
do_something_with_policy()
query_something_about_policy()
make_a_plot_with(epoch_stat)
display(info)
```
- epoch int: the epoch number
- epoch_stat dict: a large collection of metrics of the current epoch, including stat
- info dict: the usual dict out of the non-generator version of the trainer
You can even iterate on several different trainers at the same time:
```python
trainer1 = OnPolicyTrainer(...)
trainer2 = OnPolicyTrainer(...)
for result1, result2, ... in zip(trainer1, trainer2, ...):
compare_results(result1, result2, ...)
```
Co-authored-by: Jiayi Weng <trinkle23897@gmail.com>
13 lines
665 B
Markdown
13 lines
665 B
Markdown
- [ ] I have marked all applicable categories:
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+ [ ] exception-raising bug
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+ [ ] RL algorithm bug
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+ [ ] documentation request (i.e. "X is missing from the documentation.")
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+ [ ] new feature request
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- [ ] I have visited the [source website](https://github.com/thu-ml/tianshou/)
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- [ ] I have searched through the [issue tracker](https://github.com/thu-ml/tianshou/issues) for duplicates
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- [ ] I have mentioned version numbers, operating system and environment, where applicable:
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```python
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import tianshou, gym, torch, numpy, sys
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print(tianshou.__version__, gym.__version__, torch.__version__, numpy.__version__, sys.version, sys.platform)
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```
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