- Fix the current bug discussed in #844 in `test_ppo.py`.
- Add warning for `ActorProb ` if both `max_action ` and
`unbounded=True` are used for model initializations.
- Add warning for PGpolicy and DDPGpolicy if they find duplicate usage
of action-bounded actor and action scaling method.
Changes:
- Disclaimer in README
- Replaced all occurences of Gym with Gymnasium
- Removed code that is now dead since we no longer need to support the
old step API
- Updated type hints to only allow new step API
- Increased required version of envpool to support Gymnasium
- Increased required version of PettingZoo to support Gymnasium
- Updated `PettingZooEnv` to only use the new step API, removed hack to
also support old API
- I had to add some `# type: ignore` comments, due to new type hinting
in Gymnasium. I'm not that familiar with type hinting but I believe that
the issue is on the Gymnasium side and we are looking into it.
- Had to update `MyTestEnv` to support `options` kwarg
- Skip NNI tests because they still use OpenAI Gym
- Also allow `PettingZooEnv` in vector environment
- Updated doc page about ReplayBuffer to also talk about terminated and
truncated flags.
Still need to do:
- Update the Jupyter notebooks in docs
- Check the entire code base for more dead code (from compatibility
stuff)
- Check the reset functions of all environments/wrappers in code base to
make sure they use the `options` kwarg
- Someone might want to check test_env_finite.py
- Is it okay to allow `PettingZooEnv` in vector environments? Might need
to update docs?
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>
