Closes#947
This removes all kwargs from all policy constructors. While doing that,
I also improved several names and added a whole lot of TODOs.
## Functional changes:
1. Added possibility to pass None as `critic2` and `critic2_optim`. In
fact, the default behavior then should cover the absolute majority of
cases
2. Added a function called `clone_optimizer` as a temporary measure to
support passing `critic2_optim=None`
## Breaking changes:
1. `action_space` is no longer optional. In fact, it already was
non-optional, as there was a ValueError in BasePolicy.init. So now
several examples were fixed to reflect that
2. `reward_normalization` removed from DDPG and children. It was never
allowed to pass it as `True` there, an error would have been raised in
`compute_n_step_reward`. Now I removed it from the interface
3. renamed `critic1` and similar to `critic`, in order to have uniform
interfaces. Note that the `critic` in DDPG was optional for the sole
reason that child classes used `critic1`. I removed this optionality
(DDPG can't do anything with `critic=None`)
4. Several renamings of fields (mostly private to public, so backwards
compatible)
## Additional changes:
1. Removed type and default declaration from docstring. This kind of
duplication is really not necessary
2. Policy constructors are now only called using named arguments, not a
fragile mixture of positional and named as before
5. Minor beautifications in typing and code
6. Generally shortened docstrings and made them uniform across all
policies (hopefully)
## Comment:
With these changes, several problems in tianshou's inheritance hierarchy
become more apparent. I tried highlighting them for future work.
---------
Co-authored-by: Dominik Jain <d.jain@appliedai.de>
Closes#914
Additional changes:
- Deprecate python below 11
- Remove 3rd party and throughput tests. This simplifies install and
test pipeline
- Remove gym compatibility and shimmy
- Format with 3.11 conventions. In particular, add `zip(...,
strict=True/False)` where possible
Since the additional tests and gym were complicating the CI pipeline
(flaky and dist-dependent), it didn't make sense to work on fixing the
current tests in this PR to then just delete them in the next one. So
this PR changes the build and removes these tests at the same time.
Preparation for #914 and #920
Changes formatting to ruff and black. Remove python 3.8
## Additional Changes
- Removed flake8 dependencies
- Adjusted pre-commit. Now CI and Make use pre-commit, reducing the
duplication of linting calls
- Removed check-docstyle option (ruff is doing that)
- Merged format and lint. In CI the format-lint step fails if any
changes are done, so it fulfills the lint functionality.
---------
Co-authored-by: Jiayi Weng <jiayi@openai.com>
# Goals of the PR
The PR introduces **no changes to functionality**, apart from improved
input validation here and there. The main goals are to reduce some
complexity of the code, to improve types and IDE completions, and to
extend documentation and block comments where appropriate. Because of
the change to the trainer interfaces, many files are affected (more
details below), but still the overall changes are "small" in a certain
sense.
## Major Change 1 - BatchProtocol
**TL;DR:** One can now annotate which fields the batch is expected to
have on input params and which fields a returned batch has. Should be
useful for reading the code. getting meaningful IDE support, and
catching bugs with mypy. This annotation strategy will continue to work
if Batch is replaced by TensorDict or by something else.
**In more detail:** Batch itself has no fields and using it for
annotations is of limited informational power. Batches with fields are
not separate classes but instead instances of Batch directly, so there
is no type that could be used for annotation. Fortunately, python
`Protocol` is here for the rescue. With these changes we can now do
things like
```python
class ActionBatchProtocol(BatchProtocol):
logits: Sequence[Union[tuple, torch.Tensor]]
dist: torch.distributions.Distribution
act: torch.Tensor
state: Optional[torch.Tensor]
class RolloutBatchProtocol(BatchProtocol):
obs: torch.Tensor
obs_next: torch.Tensor
info: Dict[str, Any]
rew: torch.Tensor
terminated: torch.Tensor
truncated: torch.Tensor
class PGPolicy(BasePolicy):
...
def forward(
self,
batch: RolloutBatchProtocol,
state: Optional[Union[dict, Batch, np.ndarray]] = None,
**kwargs: Any,
) -> ActionBatchProtocol:
```
The IDE and mypy are now very helpful in finding errors and in
auto-completion, whereas before the tools couldn't assist in that at
all.
## Major Change 2 - remove duplication in trainer package
**TL;DR:** There was a lot of duplication between `BaseTrainer` and its
subclasses. Even worse, it was almost-duplication. There was also
interface fragmentation through things like `onpolicy_trainer`. Now this
duplication is gone and all downstream code was adjusted.
**In more detail:** Since this change affects a lot of code, I would
like to explain why I thought it to be necessary.
1. The subclasses of `BaseTrainer` just duplicated docstrings and
constructors. What's worse, they changed the order of args there, even
turning some kwargs of BaseTrainer into args. They also had the arg
`learning_type` which was passed as kwarg to the base class and was
unused there. This made things difficult to maintain, and in fact some
errors were already present in the duplicated docstrings.
2. The "functions" a la `onpolicy_trainer`, which just called the
`OnpolicyTrainer.run`, not only introduced interface fragmentation but
also completely obfuscated the docstring and interfaces. They themselves
had no dosctring and the interface was just `*args, **kwargs`, which
makes it impossible to understand what they do and which things can be
passed without reading their implementation, then reading the docstring
of the associated class, etc. Needless to say, mypy and IDEs provide no
support with such functions. Nevertheless, they were used everywhere in
the code-base. I didn't find the sacrifices in clarity and complexity
justified just for the sake of not having to write `.run()` after
instantiating a trainer.
3. The trainers are all very similar to each other. As for my
application I needed a new trainer, I wanted to understand their
structure. The similarity, however, was hard to discover since they were
all in separate modules and there was so much duplication. I kept
staring at the constructors for a while until I figured out that
essentially no changes to the superclass were introduced. Now they are
all in the same module and the similarities/differences between them are
much easier to grasp (in my opinion)
4. Because of (1), I had to manually change and check a lot of code,
which was very tedious and boring. This kind of work won't be necessary
in the future, since now IDEs can be used for changing signatures,
renaming args and kwargs, changing class names and so on.
I have some more reasons, but maybe the above ones are convincing
enough.
## Minor changes: improved input validation and types
I added input validation for things like `state` and `action_scaling`
(which only makes sense for continuous envs). After adding this, some
tests failed to pass this validation. There I added
`action_scaling=isinstance(env.action_space, Box)`, after which tests
were green. I don't know why the tests were green before, since action
scaling doesn't make sense for discrete actions. I guess some aspect was
not tested and didn't crash.
I also added Literal in some places, in particular for
`action_bound_method`. Now it is no longer allowed to pass an empty
string, instead one should pass `None`. Also here there is input
validation with clear error messages.
@Trinkle23897 The functional tests are green. I didn't want to fix the
formatting, since it will change in the next PR that will solve #914
anyway. I also found a whole bunch of code in `docs/_static`, which I
just deleted (shouldn't it be copied from the sources during docs build
instead of committed?). I also haven't adjusted the documentation yet,
which atm still mentions the trainers of the type
`onpolicy_trainer(...)` instead of `OnpolicyTrainer(...).run()`
## Breaking Changes
The adjustments to the trainer package introduce breaking changes as
duplicated interfaces are deleted. However, it should be very easy for
users to adjust to them
---------
Co-authored-by: Michael Panchenko <m.panchenko@appliedai.de>
**The assert was missing a description, I fixed it.**
Please note: there is an error in the documentations, but it does not
seem to be related to my changes.
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?
## implementation
I implemented HER solely as a replay buffer. It is done by temporarily
directly re-writing transitions storage (`self._meta`) during the
`sample_indices()` call. The original transitions are cached and will be
restored at the beginning of the next sampling or when other methods is
called. This will make sure that. for example, n-step return calculation
can be done without altering the policy.
There is also a problem with the original indices sampling. The sampled
indices are not guaranteed to be from different episodes. So I decided
to perform re-writing based on the episode. This guarantees that the
sampled transitions from the same episode will have the same re-written
goal. This also make the re-writing ratio calculation slightly differ
from the paper, but it won't be too different if there are many episodes
in the buffer.
In the current commit, HER replay buffer only support 'future' strategy
and online sampling. This is the best of HER in term of performance and
memory efficiency.
I also add a few more convenient replay buffers
(`HERVectorReplayBuffer`, `HERReplayBufferManager`), test env
(`MyGoalEnv`), gym wrapper (`TruncatedAsTerminated`), unit tests, and a
simple example (examples/offline/fetch_her_ddpg.py).
## verification
I have added unit tests for almost everything I have implemented.
HER replay buffer was also tested using DDPG on [`FetchReach-v3`
env](https://github.com/Farama-Foundation/Gymnasium-Robotics). I used
default DDPG parameters from mujoco example and didn't tune anything
further to get this good result! (train script:
examples/offline/fetch_her_ddpg.py).
- This PR adds the checks that are defined in the Makefile as pre-commit
hooks.
- Hopefully, the checks are equivalent to those from the Makefile, but I
can't guarantee it.
- CI remains as it is.
- As I pointed out on discord, I experienced some conflicts between
flake8 and yapf, so it might be better to transition to some other
combination (e.g. black).
* 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)
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
- change the internal API name of worker: send_action -> send, get_result -> recv (align with envpool)
- add a timing test for venvs.reset() to make sure the concurrent execution
- change venvs.reset() logic
Co-authored-by: Jiayi Weng <trinkle23897@gmail.com>
- Batch: do not raise error when it finds list of np.array with different shape[0].
- Venv's obs: add try...except block for np.stack(obs_list)
- remove venv.__del__ since it is buggy
Change the behavior of to_numpy and to_torch: from now on, dict is automatically converted to Batch and list is automatically converted to np.ndarray (if an error occurs, raise the exception instead of converting each element in the list).
Cherry-pick from #200
- update the function signature
- format code-style
- move _compile into separate functions
- fix a bug in to_torch and to_numpy (Batch)
- remove None in action_range
In short, the code-format only contains function-signature style and `'` -> `"`. (pick up from [black](https://github.com/psf/black))
1. add policy.eval() in all test scripts' "watch performance"
2. remove dict return support for collector preprocess_fn
3. add `__contains__` and `pop` in batch: `key in batch`, `batch.pop(key, deft)`
4. exact n_episode for a list of n_episode limitation and save fake data in cache_buffer when self.buffer is None (#184)
5. fix tensorboard logging: h-axis stands for env step instead of gradient step; add test results into tensorboard
6. add test_returns (both GAE and nstep)
7. change the type-checking order in batch.py and converter.py in order to meet the most often case first
8. fix shape inconsistency for torch.Tensor in replay buffer
9. remove `**kwargs` in ReplayBuffer
10. remove default value in batch.split() and add merge_last argument (#185)
11. improve nstep efficiency
12. add max_batchsize in onpolicy algorithms
13. potential bugfix for subproc.wait
14. fix RecurrentActorProb
15. improve the code-coverage (from 90% to 95%) and remove the dead code
16. fix some incorrect type annotation
The above improvement also increases the training FPS: on my computer, the previous version is only ~1800 FPS and after that, it can reach ~2050 (faster than v0.2.4.post1).
- Refacor code to remove duplicate code
- Enable async simulation for all vector envs
- Remove `collector.close` and rename `VectorEnv` to `DummyVectorEnv`
The abstraction of vector env changed.
Prior to this pr, each vector env is almost independent.
After this pr, each env is wrapped into a worker, and vector envs differ with their worker type. In fact, users can just use `BaseVectorEnv` with different workers, I keep `SubprocVectorEnv`, `ShmemVectorEnv` for backward compatibility.
Co-authored-by: n+e <463003665@qq.com>
Co-authored-by: magicly <magicly007@gmail.com>
* make fileds with empty Batch rather than None after reset
* dummy code
* remove dummy
* add reward_length argument for collector
* Improve Batch (#126)
* make sure the key type of Batch is string, and add unit tests
* add is_empty() function and unit tests
* enable cat of mixing dict and Batch, just like stack
* bugfix for reward_length
* add get_final_reward_fn argument to collector to deal with marl
* minor polish
* remove multibuf
* minor polish
* improve and implement Batch.cat_
* bugfix for buffer.sample with field impt_weight
* restore the usage of a.cat_(b)
* fix 2 bugs in batch and add corresponding unittest
* code fix for update
* update is_empty to recognize empty over empty; bugfix for len
* bugfix for update and add testcase
* add testcase of update
* make fileds with empty Batch rather than None after reset
* dummy code
* remove dummy
* add reward_length argument for collector
* bugfix for reward_length
* add get_final_reward_fn argument to collector to deal with marl
* make sure the key type of Batch is string, and add unit tests
* add is_empty() function and unit tests
* enable cat of mixing dict and Batch, just like stack
* dummy code
* remove dummy
* add multi-agent example: tic-tac-toe
* move TicTacToeEnv to a separate file
* remove dummy MANet
* code refactor
* move tic-tac-toe example to test
* update doc with marl-example
* fix docs
* reduce the threshold
* revert
* update player id to start from 1 and change player to agent; keep coding
* add reward_length argument for collector
* Improve Batch (#128)
* minor polish
* improve and implement Batch.cat_
* bugfix for buffer.sample with field impt_weight
* restore the usage of a.cat_(b)
* fix 2 bugs in batch and add corresponding unittest
* code fix for update
* update is_empty to recognize empty over empty; bugfix for len
* bugfix for update and add testcase
* add testcase of update
* fix docs
* fix docs
* fix docs [ci skip]
* fix docs [ci skip]
Co-authored-by: Trinkle23897 <463003665@qq.com>
* refact
* re-implement Batch.stack and add testcases
* add doc for Batch.stack
* reward_metric
* modify flag
* minor fix
* reuse _create_values and refactor stack_ & cat_
* fix pep8
* fix reward stat in collector
* fix stat of collector, simplify test/base/env.py
* fix docs
* minor fix
* raise exception for stacking with partial keys and axis!=0
* minor fix
* minor fix
* minor fix
* marl-examples
* add condense; bugfix for torch.Tensor; code refactor
* marl example can run now
* enable tic tac toe with larger board size and win-size
* add test dependency
* Fix padding of inconsistent keys with Batch.stack and Batch.cat (#130)
* re-implement Batch.stack and add testcases
* add doc for Batch.stack
* reuse _create_values and refactor stack_ & cat_
* fix pep8
* fix docs
* raise exception for stacking with partial keys and axis!=0
* minor fix
* minor fix
Co-authored-by: Trinkle23897 <463003665@qq.com>
* stash
* let agent learn to play as agent 2 which is harder
* code refactor
* Improve collector (#125)
* remove multibuf
* reward_metric
* make fileds with empty Batch rather than None after reset
* many fixes and refactor
Co-authored-by: Trinkle23897 <463003665@qq.com>
* marl for tic-tac-toe and general gomoku
* update default gamma to 0.1 for tic tac toe to win earlier
* fix name typo; change default game config; add rew_norm option
* fix pep8
* test commit
* mv test dir name
* add rew flag
* fix torch.optim import error and madqn rew_norm
* remove useless kwargs
* Vector env enable select worker (#132)
* Enable selecting worker for vector env step method.
* Update collector to match new vecenv selective worker behavior.
* Bug fix.
* Fix rebase
Co-authored-by: Alexis Duburcq <alexis.duburcq@wandercraft.eu>
* show the last move of tictactoe by capital letters
* add multi-agent tutorial
* fix link
* Standardized behavior of Batch.cat and misc code refactor (#137)
* code refactor; remove unused kwargs; add reward_normalization for dqn
* bugfix for __setitem__ with torch.Tensor; add Batch.condense
* minor fix
* support cat with empty Batch
* remove the dependency of is_empty on len; specify the semantic of empty Batch by test cases
* support stack with empty Batch
* remove condense
* refactor code to reflect the shared / partial / reserved categories of keys
* add is_empty(recursive=False)
* doc fix
* docfix and bugfix for _is_batch_set
* add doc for key reservation
* bugfix for algebra operators
* fix cat with lens hint
* code refactor
* bugfix for storing None
* use ValueError instead of exception
* hide lens away from users
* add comment for __cat
* move the computation of the initial value of lens in cat_ itself.
* change the place of doc string
* doc fix for Batch doc string
* change recursive to recurse
* doc string fix
* minor fix for batch doc
* write tutorials to specify the standard of Batch (#142)
* add doc for len exceptions
* doc move; unify is_scalar_value function
* remove some issubclass check
* bugfix for shape of Batch(a=1)
* keep moving doc
* keep writing batch tutorial
* draft version of Batch tutorial done
* improving doc
* keep improving doc
* batch tutorial done
* rename _is_number
* rename _is_scalar
* shape property do not raise exception
* restore some doc string
* grammarly [ci skip]
* grammarly + fix warning of building docs
* polish docs
* trim and re-arrange batch tutorial
* go straight to the point
* minor fix for batch doc
* add shape / len in basic usage
* keep improving tutorial
* unify _to_array_with_correct_type to remove duplicate code
* delegate type convertion to Batch.__init__
* further delegate type convertion to Batch.__init__
* bugfix for setattr
* add a _parse_value function
* remove dummy function call
* polish docs
Co-authored-by: Trinkle23897 <463003665@qq.com>
* bugfix for mapolicy
* pretty code
* remove debug code; remove condense
* doc fix
* check before get_agents in tutorials/tictactoe
* tutorial
* fix
* minor fix for batch doc
* minor polish
* faster test_ttt
* improve tic-tac-toe environment
* change default epoch and step-per-epoch for tic-tac-toe
* fix mapolicy
* minor polish for mapolicy
* 90% to 80% (need to change the tutorial)
* win rate
* show step number at board
* simplify mapolicy
* minor polish for mapolicy
* remove MADQN
* fix pep8
* change legal_actions to mask (need to update docs)
* simplify maenv
* fix typo
* move basevecenv to single file
* separate RandomAgent
* update docs
* grammarly
* fix pep8
* win rate typo
* format in cheatsheet
* use bool mask directly
* update doc for boolean mask
Co-authored-by: Trinkle23897 <463003665@qq.com>
Co-authored-by: Alexis DUBURCQ <alexis.duburcq@gmail.com>
Co-authored-by: Alexis Duburcq <alexis.duburcq@wandercraft.eu>
