## 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).
- 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.
This PR focus on refactor of logging method to solve bug of nan reward and log interval. After these two pr, hopefully fundamental change of tianshou/data is finished. We then can concentrate on building benchmarks of tianshou finally.
Things changed:
1. trainer now accepts logger (BasicLogger or LazyLogger) instead of writer;
2. remove utils.SummaryWriter;
This PR focus on some definition change of trainer to make it more friendly to use and be consistent with typical usage in research papers, typically change `collect-per-step` to `step-per-collect`, add `update-per-step` / `episode-per-collect` accordingly, and modify the documentation.
This is the third PR of 6 commits mentioned in #274, which features refactor of Collector to fix#245. You can check #274 for more detail.
Things changed in this PR:
1. refactor collector to be more cleaner, split AsyncCollector to support asyncvenv;
2. change buffer.add api to add(batch, bffer_ids); add several types of buffer (VectorReplayBuffer, PrioritizedVectorReplayBuffer, etc.)
3. add policy.exploration_noise(act, batch) -> act
4. small change in BasePolicy.compute_*_returns
5. move reward_metric from collector to trainer
6. fix np.asanyarray issue (different version's numpy will result in different output)
7. flake8 maxlength=88
8. polish docs and fix test
Co-authored-by: n+e <trinkle23897@gmail.com>
This is the first commit of 6 commits mentioned in #274, which features
1. Refactor of `Class Net` to support any form of MLP.
2. Enable type check in utils.network.
3. Relative change in docs/test/examples.
4. Move atari-related network to examples/atari/atari_network.py
Co-authored-by: Trinkle23897 <trinkle23897@gmail.com>
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>
