The new proposed feature is to have trainers as generators.
The usage pattern is:
```python
trainer = OnPolicyTrainer(...)
for epoch, epoch_stat, info in trainer:
print(f"Epoch: {epoch}")
print(epoch_stat)
print(info)
do_something_with_policy()
query_something_about_policy()
make_a_plot_with(epoch_stat)
display(info)
```
- epoch int: the epoch number
- epoch_stat dict: a large collection of metrics of the current epoch, including stat
- info dict: the usual dict out of the non-generator version of the trainer
You can even iterate on several different trainers at the same time:
```python
trainer1 = OnPolicyTrainer(...)
trainer2 = OnPolicyTrainer(...)
for result1, result2, ... in zip(trainer1, trainer2, ...):
compare_results(result1, result2, ...)
```
Co-authored-by: Jiayi Weng <trinkle23897@gmail.com>
* Use `global_step` as the x-axis for wandb
* Use Tensorboard SummaryWritter as core with `wandb.init(..., sync_tensorboard=True)`
* Update all atari examples with wandb
Co-authored-by: Jiayi Weng <trinkle23897@gmail.com>
- 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
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 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 PR for QR-DQN algorithm: https://arxiv.org/abs/1710.10044
1. add QR-DQN policy in tianshou/policy/modelfree/qrdqn.py.
2. add QR-DQN net in examples/atari/atari_network.py.
3. add QR-DQN atari example in examples/atari/atari_qrdqn.py.
4. add QR-DQN statement in tianshou/policy/init.py.
5. add QR-DQN unit test in test/discrete/test_qrdqn.py.
6. add QR-DQN atari results in examples/atari/results/qrdqn/.
7. add compute_q_value in DQNPolicy and C51Policy for simplify forward function.
8. move `with torch.no_grad():` from `_target_q` to BasePolicy
By running "python3 atari_qrdqn.py --task "PongNoFrameskip-v4" --batch-size 64", get best_result': '19.8 ± 0.40', in epoch 8.
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
