* Changes to support Gym 0.26.0
* Replace map by simpler list comprehension
* Use syntax that is compatible with python 3.7
* Format code
* Fix environment seeding in test environment, fix buffer_profile test
* Remove self.seed() from __init__
* Fix random number generation
* Fix throughput tests
* Fix tests
* Removed done field from Buffer, fixed throughput test, turned off wandb, fixed formatting, fixed type hints, allow preprocessing_fn with truncated and terminated arguments, updated docstrings
* fix lint
* fix
* fix import
* fix
* fix mypy
* pytest --ignore='test/3rd_party'
* Use correct step API in _SetAttrWrapper
* Format
* Fix mypy
* Format
* Fix pydocstyle.
fixes some deprecation warnings due to new changes in gym version 0.23:
- use `env.np_random.integers` instead of `env.np_random.randint`
- support `seed` and `return_info` arguments for reset (addresses https://github.com/thu-ml/tianshou/issues/605)
* When clip_loss_grad=True is passed, Huber loss is used instead of the MSE loss.
* Made the argument's name more descriptive;
* Replaced the smooth L1 loss with the Huber loss, which has an identical form to the default parametrization, but seems to be better known in this context;
* Added a fuller description to the docstring;
- A DummyTqdm class added to utils: it replicates the interface used by trainers, but does not show the progress bar;
- Added a show_progress argument to the base trainer: when show_progress == True, dummy_tqdm is used in place of tqdm.
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>
(Issue #512) Random start in Collector sample actions from the action space, while policies output action in a range (typically [-1, 1]) and map action to the action space. The buffer only stores unmapped actions, so the actions randomly initialized are not correct when the action range is not [-1, 1]. This may influence policy learning and particularly model learning in model-based methods.
This PR fixes it by adding an inverse operation before adding random initial actions to the buffer.
add imitation baselines for offline RL; make the choice of env/task and D4RL dataset explicit; on expert datasets, IL easily outperforms; after reading the D4RL paper, I'll rerun the exps on medium data
* Use `global_step` as the x-axis for wandb
* Use Tensorboard SummaryWritter as core with `wandb.init(..., sync_tensorboard=True)`
* Update all atari examples with wandb
Co-authored-by: Jiayi Weng <trinkle23897@gmail.com>
