- 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
