- 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>
This PR implements BCQPolicy, which could be used to train an offline agent in the environment of continuous action space. An experimental result 'halfcheetah-expert-v1' is provided, which is a d4rl environment (for Offline Reinforcement Learning).
Example usage is in the examples/offline/offline_bcq.py.
- 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 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 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 second commit of 6 commits mentioned in #274, which features minor refactor of ReplayBuffer and adding two new ReplayBuffer classes called CachedReplayBuffer and ReplayBufferManager. You can check #274 for more detail.
1. Add ReplayBufferManager (handle a list of buffers) and CachedReplayBuffer;
2. Make sure the reserved keys cannot be edited by methods like `buffer.done = xxx`;
3. Add `set_batch` method for manually choosing the batch the ReplayBuffer wants to handle;
4. Add `sample_index` method, same as `sample` but only return index instead of both index and batch data;
5. Add `prev` (one-step previous transition index), `next` (one-step next transition index) and `unfinished_index` (the last modified index whose done==False);
6. Separate `alloc_fn` method for allocating new memory for `self._meta` when a new `(key, value)` pair comes in;
7. Move buffer's documentation to `docs/tutorials/concepts.rst`.
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>
This is the PR for C51algorithm: https://arxiv.org/abs/1707.06887
1. add C51 policy in tianshou/policy/modelfree/c51.py.
2. add C51 net in tianshou/utils/net/discrete.py.
3. add C51 atari example in examples/atari/atari_c51.py.
4. add C51 statement in tianshou/policy/__init__.py.
5. add C51 test in test/discrete/test_c51.py.
6. add C51 atari results in examples/atari/results/c51/.
By running "python3 atari_c51.py --task "PongNoFrameskip-v4" --batch-size 64", get best_result': '20.50 ± 0.50', in epoch 9.
By running "python3 atari_c51.py --task "BreakoutNoFrameskip-v4" --n-step 1 --epoch 40", get best_reward: 407.400000 ± 31.155096 in epoch 39.
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
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
This PR aims to provide the script of Atari DQN setting:
- A speedrun of PongNoFrameskip-v4 (finished, about half an hour in i7-8750 + GTX1060 with 1M environment steps)
- A general script for all atari game
Since we use multiple env for simulation, the result is slightly different from the original paper, but consider to be acceptable.
It also adds another parameter save_only_last_obs for replay buffer in order to save the memory.
Co-authored-by: Trinkle23897 <463003665@qq.com>
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>
* add policy.update to enable post process and remove collector.sample
* update doc in policy concept
* remove collector.sample in doc
* doc update of concepts
* docs
* polish
* polish policy
* remove collector.sample in docs
* minor fix
* Apply suggestions from code review
just a test
* doc fix
Co-authored-by: Trinkle23897 <463003665@qq.com>
Unify the implementation with multi-environments (wrap a single environment in a multi-environment with one envs) to greatly simplify the code.
This changed the behavior of single-environment.
Prior to this pr, for single environment, collector.collect(n_step=n) will step n steps.
After this pr, for single environment, collector.collect(n_step=n) will step m episodes until the steps are greater than n.
That is to say, collectors now always collect full episodes.
* 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>
* 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
* 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>
* 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>
* 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
