- 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).
This PR separates the `global_step` into `env_step` and `gradient_step`. In the future, the data from the collecting state will be stored under `env_step`, and the data from the updating state will be stored under `gradient_step`.
Others:
- add `rew_std` and `best_result` into the monitor
- fix network unbounded in `test/continuous/test_sac_with_il.py` and `examples/box2d/bipedal_hardcore_sac.py`
- change the dependency of ray to 1.0.0 since ray-project/ray#10134 has been resolved
- fix a bug in MAPolicy: `buffer.rew = Batch()` doesn't change `buffer.rew` (thanks mypy)
- polish examples/box2d/bipedal_hardcore_sac.py
- several docs update
- format setup.py and bump version to 0.2.7
Training FPS improvement (base commit is 94bfb32):
test_pdqn: 1660 (without numba) -> 1930
discrete/test_ppo: 5100 -> 5170
since nstep has little impact on overall performance, the unit test result is:
GAE: 4.1s -> 0.057s
nstep: 0.3s -> 0.15s (little improvement)
Others:
- fix a bug in ttt set_eps
- keep only sumtree in segment tree implementation
- dirty fix for asyncVenv check_id test
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>
- fix 2 warning in doctest
- change the minimum version of gym (to be aligned with openai baselines)
- change squeeze and reshape to flatten (related to #155). I think flatten is better.
* add_pybullet_ens_test
test on pybullet envs
modify some log config
* delete DS_Store file
* add pybullet_envs test
add HalfCheetahBulletEnv-v0 test
modify log config
* fix pep 8 errors
* add pybullet to dev
* delete a line
* by pass F401
* add log_interval to onpolicy_trainer
* add comments
* Update halfcheetahBullet_v0_sac.py
* update atari.py
* fix setup.py
pass the pytest
* fix setup.py
pass the pytest
* add args "render"
* change the tensorboard writter
* change the tensorboard writter
* change device, render, tensorboard log location
* change device, render, tensorboard log location
* remove some wrong local files
* fix some tab mistakes and the envs name in continuous/test_xx.py
* add examples and point robot maze environment
* fix some bugs during testing examples
* add dqn network and fix some args
* change back the tensorboard writter's frequency to ensure ppo and a2c can write things normally
* add a warning to collector
* rm some unrelated files
* reformat
* fix a bug in test_dqn due to the model wrong selection
