522f7fbf98
This PR adds strict typing to the output of `update` and `learn` in all
policies. This will likely be the last large refactoring PR before the
next release (0.6.0, not 1.0.0), so it requires some attention. Several
difficulties were encountered on the path to that goal:
1. The policy hierarchy is actually "broken" in the sense that the keys
of dicts that were output by `learn` did not follow the same enhancement
(inheritance) pattern as the policies. This is a real problem and should
be addressed in the near future. Generally, several aspects of the
policy design and hierarchy might deserve a dedicated discussion.
2. Each policy needs to be generic in the stats return type, because one
might want to extend it at some point and then also extend the stats.
Even within the source code base this pattern is necessary in many
places.
3. The interaction between learn and update is a bit quirky, we
currently handle it by having update modify special field inside
TrainingStats, whereas all other fields are handled by learn.
4. The IQM module is a policy wrapper and required a
TrainingStatsWrapper. The latter relies on a bunch of black magic.
They were addressed by:
1. Live with the broken hierarchy, which is now made visible by bounds
in generics. We use type: ignore where appropriate.
2. Make all policies generic with bounds following the policy
inheritance hierarchy (which is incorrect, see above). We experimented a
bit with nested TrainingStats classes, but that seemed to add more
complexity and be harder to understand. Unfortunately, mypy thinks that
the code below is wrong, wherefore we have to add `type: ignore` to the
return of each `learn`
```python
T = TypeVar("T", bound=int)
def f() -> T:
return 3
```
3. See above
4. Write representative tests for the `TrainingStatsWrapper`. Still, the
black magic might cause nasty surprises down the line (I am not proud of
it)...
Closes #933
---------
Co-authored-by: Maximilian Huettenrauch <m.huettenrauch@appliedai.de>
Co-authored-by: Michael Panchenko <m.panchenko@appliedai.de>
655 lines
26 KiB
Python
655 lines
26 KiB
Python
import logging
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import time
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from abc import ABC, abstractmethod
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from collections import defaultdict, deque
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from collections.abc import Callable
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from dataclasses import asdict
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from typing import Any
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import numpy as np
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import tqdm
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from tianshou.data import (
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AsyncCollector,
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Collector,
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CollectStats,
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EpochStats,
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InfoStats,
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ReplayBuffer,
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SequenceSummaryStats,
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)
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from tianshou.data.collector import CollectStatsBase
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from tianshou.policy import BasePolicy
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from tianshou.policy.base import TrainingStats
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from tianshou.trainer.utils import gather_info, test_episode
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from tianshou.utils import (
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BaseLogger,
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DummyTqdm,
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LazyLogger,
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MovAvg,
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deprecation,
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tqdm_config,
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)
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from tianshou.utils.logging import set_numerical_fields_to_precision
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log = logging.getLogger(__name__)
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class BaseTrainer(ABC):
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"""An iterator base class for trainers.
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Returns an iterator that yields a 3-tuple (epoch, stats, info) of train results
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on every epoch.
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:param policy: an instance of the :class:`~tianshou.policy.BasePolicy` class.
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:param batch_size: the batch size of sample data, which is going to feed in
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the policy network. If None, will use the whole buffer in each gradient step.
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:param train_collector: the collector used for training.
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:param test_collector: the collector used for testing. If it's None,
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then no testing will be performed.
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:param buffer: the replay buffer used for off-policy algorithms or for pre-training.
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If a policy overrides the ``process_buffer`` method, the replay buffer will
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be pre-processed before training.
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:param max_epoch: the maximum number of epochs for training. The training
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process might be finished before reaching ``max_epoch`` if ``stop_fn``
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is set.
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:param step_per_epoch: the number of transitions collected per epoch.
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:param repeat_per_collect: the number of repeat time for policy learning,
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for example, set it to 2 means the policy needs to learn each given batch
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data twice. Only used in on-policy algorithms
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:param episode_per_test: the number of episodes for one policy evaluation.
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:param update_per_step: only used in off-policy algorithms.
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How many gradient steps to perform per step in the environment
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(i.e., per sample added to the buffer).
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:param step_per_collect: the number of transitions the collector would
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collect before the network update, i.e., trainer will collect
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"step_per_collect" transitions and do some policy network update repeatedly
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in each epoch.
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:param episode_per_collect: the number of episodes the collector would
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collect before the network update, i.e., trainer will collect
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"episode_per_collect" episodes and do some policy network update repeatedly
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in each epoch.
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:param train_fn: a hook called at the beginning of training in each
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epoch. It can be used to perform custom additional operations, with the
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signature ``f(num_epoch: int, step_idx: int) -> None``.
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:param test_fn: a hook called at the beginning of testing in each
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epoch. It can be used to perform custom additional operations, with the
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signature ``f(num_epoch: int, step_idx: int) -> None``.
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:param save_best_fn: a hook called when the undiscounted average mean
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reward in evaluation phase gets better, with the signature
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``f(policy: BasePolicy) -> None``. It was ``save_fn`` previously.
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:param save_checkpoint_fn: a function to save training process and
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return the saved checkpoint path, with the signature ``f(epoch: int,
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env_step: int, gradient_step: int) -> str``; you can save whatever you want.
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:param resume_from_log: resume env_step/gradient_step and other metadata
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from existing tensorboard log.
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:param stop_fn: a function with signature ``f(mean_rewards: float) ->
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bool``, receives the average undiscounted returns of the testing result,
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returns a boolean which indicates whether reaching the goal.
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:param reward_metric: a function with signature
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``f(rewards: np.ndarray with shape (num_episode, agent_num)) -> np.ndarray
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with shape (num_episode,)``, used in multi-agent RL. We need to return a
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single scalar for each episode's result to monitor training in the
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multi-agent RL setting. This function specifies what is the desired metric,
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e.g., the reward of agent 1 or the average reward over all agents.
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:param logger: A logger that logs statistics during
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training/testing/updating. To not log anything, keep the default logger.
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:param verbose: whether to print status information to stdout.
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If set to False, status information will still be logged (provided that
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logging is enabled via the `logging` module).
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:param show_progress: whether to display a progress bar when training.
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:param test_in_train: whether to test in the training phase.
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"""
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__doc__: str
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@staticmethod
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def gen_doc(learning_type: str) -> str:
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"""Document string for subclass trainer."""
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step_means = f'The "step" in {learning_type} trainer means '
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if learning_type != "offline":
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step_means += "an environment step (a.k.a. transition)."
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else: # offline
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step_means += "a gradient step."
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trainer_name = learning_type.capitalize() + "Trainer"
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return f"""An iterator class for {learning_type} trainer procedure.
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Returns an iterator that yields a 3-tuple (epoch, stats, info) of
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train results on every epoch.
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{step_means}
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Example usage:
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::
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trainer = {trainer_name}(...)
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for epoch, epoch_stat, info in trainer:
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print("Epoch:", epoch)
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print(epoch_stat)
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print(info)
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do_something_with_policy()
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query_something_about_policy()
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make_a_plot_with(epoch_stat)
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display(info)
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- epoch int: the epoch number
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- epoch_stat dict: a large collection of metrics of the current epoch
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- info dict: result returned from :func:`~tianshou.trainer.gather_info`
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You can even iterate on several trainers at the same time:
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::
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trainer1 = {trainer_name}(...)
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trainer2 = {trainer_name}(...)
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for result1, result2, ... in zip(trainer1, trainer2, ...):
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compare_results(result1, result2, ...)
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"""
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def __init__(
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self,
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policy: BasePolicy,
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max_epoch: int,
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batch_size: int | None,
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train_collector: Collector | None = None,
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test_collector: Collector | None = None,
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buffer: ReplayBuffer | None = None,
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step_per_epoch: int | None = None,
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repeat_per_collect: int | None = None,
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episode_per_test: int | None = None,
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update_per_step: float = 1.0,
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step_per_collect: int | None = None,
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episode_per_collect: int | None = None,
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train_fn: Callable[[int, int], None] | None = None,
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test_fn: Callable[[int, int | None], None] | None = None,
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stop_fn: Callable[[float], bool] | None = None,
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save_best_fn: Callable[[BasePolicy], None] | None = None,
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save_checkpoint_fn: Callable[[int, int, int], str] | None = None,
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resume_from_log: bool = False,
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reward_metric: Callable[[np.ndarray], np.ndarray] | None = None,
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logger: BaseLogger = LazyLogger(),
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verbose: bool = True,
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show_progress: bool = True,
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test_in_train: bool = True,
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save_fn: Callable[[BasePolicy], None] | None = None,
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):
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if save_fn:
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deprecation(
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"save_fn in trainer is marked as deprecated and will be "
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"removed in the future. Please use save_best_fn instead.",
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)
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assert save_best_fn is None
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save_best_fn = save_fn
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self.policy = policy
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if buffer is not None:
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buffer = policy.process_buffer(buffer)
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self.buffer = buffer
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self.train_collector = train_collector
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self.test_collector = test_collector
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self.logger = logger
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self.start_time = time.time()
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self.stat: defaultdict[str, MovAvg] = defaultdict(MovAvg)
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self.best_reward = 0.0
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self.best_reward_std = 0.0
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self.start_epoch = 0
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# This is only used for logging but creeps into the implementations
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# of the trainers. I believe it would be better to remove
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self._gradient_step = 0
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self.env_step = 0
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self.policy_update_time = 0.0
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self.max_epoch = max_epoch
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self.step_per_epoch = step_per_epoch
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# either on of these two
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self.step_per_collect = step_per_collect
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self.episode_per_collect = episode_per_collect
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self.update_per_step = update_per_step
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self.repeat_per_collect = repeat_per_collect
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self.episode_per_test = episode_per_test
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self.batch_size = batch_size
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self.train_fn = train_fn
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self.test_fn = test_fn
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self.stop_fn = stop_fn
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self.save_best_fn = save_best_fn
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self.save_checkpoint_fn = save_checkpoint_fn
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self.reward_metric = reward_metric
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self.verbose = verbose
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self.show_progress = show_progress
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self.test_in_train = test_in_train
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self.resume_from_log = resume_from_log
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self.is_run = False
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self.last_rew, self.last_len = 0.0, 0.0
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self.epoch = self.start_epoch
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self.best_epoch = self.start_epoch
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self.stop_fn_flag = False
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self.iter_num = 0
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def reset(self) -> None:
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"""Initialize or reset the instance to yield a new iterator from zero."""
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self.is_run = False
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self.env_step = 0
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if self.resume_from_log:
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(
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self.start_epoch,
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self.env_step,
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self._gradient_step,
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) = self.logger.restore_data()
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self.last_rew, self.last_len = 0.0, 0.0
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self.start_time = time.time()
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if self.train_collector is not None:
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self.train_collector.reset_stat()
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if self.train_collector.policy != self.policy or self.test_collector is None:
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self.test_in_train = False
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if self.test_collector is not None:
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assert self.episode_per_test is not None
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assert not isinstance(self.test_collector, AsyncCollector) # Issue 700
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self.test_collector.reset_stat()
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test_result = test_episode(
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self.policy,
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self.test_collector,
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self.test_fn,
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self.start_epoch,
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self.episode_per_test,
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self.logger,
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self.env_step,
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self.reward_metric,
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)
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assert test_result.returns_stat is not None # for mypy
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self.best_epoch = self.start_epoch
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self.best_reward, self.best_reward_std = (
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test_result.returns_stat.mean,
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test_result.returns_stat.std,
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)
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if self.save_best_fn:
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self.save_best_fn(self.policy)
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self.epoch = self.start_epoch
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self.stop_fn_flag = False
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self.iter_num = 0
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def __iter__(self): # type: ignore
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self.reset()
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return self
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def __next__(self) -> EpochStats:
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"""Perform one epoch (both train and eval)."""
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self.epoch += 1
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self.iter_num += 1
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if self.iter_num > 1:
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# iterator exhaustion check
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if self.epoch > self.max_epoch:
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raise StopIteration
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# exit flag 1, when stop_fn succeeds in train_step or test_step
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if self.stop_fn_flag:
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raise StopIteration
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# set policy in train mode
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self.policy.train()
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progress = tqdm.tqdm if self.show_progress else DummyTqdm
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# perform n step_per_epoch
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with progress(total=self.step_per_epoch, desc=f"Epoch #{self.epoch}", **tqdm_config) as t:
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while t.n < t.total and not self.stop_fn_flag:
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train_stat: CollectStatsBase
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if self.train_collector is not None:
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pbar_data_dict, train_stat, self.stop_fn_flag = self.train_step()
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t.update(train_stat.n_collected_steps)
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if self.stop_fn_flag:
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t.set_postfix(**pbar_data_dict)
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break
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else:
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pbar_data_dict = {}
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assert self.buffer, "No train_collector or buffer specified"
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train_stat = CollectStatsBase(
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n_collected_episodes=len(self.buffer),
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n_collected_steps=int(self._gradient_step),
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)
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t.update()
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update_stat = self.policy_update_fn(train_stat)
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pbar_data_dict = set_numerical_fields_to_precision(pbar_data_dict)
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pbar_data_dict["gradient_step"] = self._gradient_step
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t.set_postfix(**pbar_data_dict)
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if t.n <= t.total and not self.stop_fn_flag:
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t.update()
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# for offline RL
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if self.train_collector is None:
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assert self.buffer is not None
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batch_size = self.batch_size or len(self.buffer)
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self.env_step = self._gradient_step * batch_size
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test_stat = None
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if not self.stop_fn_flag:
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self.logger.save_data(
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self.epoch,
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self.env_step,
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self._gradient_step,
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self.save_checkpoint_fn,
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)
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# test
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if self.test_collector is not None:
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test_stat, self.stop_fn_flag = self.test_step()
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info_stat = gather_info(
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start_time=self.start_time,
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policy_update_time=self.policy_update_time,
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gradient_step=self._gradient_step,
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best_reward=self.best_reward,
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best_reward_std=self.best_reward_std,
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train_collector=self.train_collector,
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test_collector=self.test_collector,
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)
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self.logger.log_info_data(asdict(info_stat), self.epoch)
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# in case trainer is used with run(), epoch_stat will not be returned
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epoch_stat: EpochStats = EpochStats(
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epoch=self.epoch,
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train_collect_stat=train_stat,
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test_collect_stat=test_stat,
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training_stat=update_stat,
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info_stat=info_stat,
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)
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return epoch_stat
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def test_step(self) -> tuple[CollectStats, bool]:
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"""Perform one testing step."""
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assert self.episode_per_test is not None
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assert self.test_collector is not None
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stop_fn_flag = False
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test_stat = test_episode(
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self.policy,
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self.test_collector,
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self.test_fn,
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self.epoch,
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self.episode_per_test,
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self.logger,
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self.env_step,
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self.reward_metric,
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)
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assert test_stat.returns_stat is not None # for mypy
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rew, rew_std = test_stat.returns_stat.mean, test_stat.returns_stat.std
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if self.best_epoch < 0 or self.best_reward < rew:
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self.best_epoch = self.epoch
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self.best_reward = float(rew)
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self.best_reward_std = rew_std
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if self.save_best_fn:
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self.save_best_fn(self.policy)
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log_msg = (
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f"Epoch #{self.epoch}: test_reward: {rew:.6f} ± {rew_std:.6f},"
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f" best_reward: {self.best_reward:.6f} ± "
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f"{self.best_reward_std:.6f} in #{self.best_epoch}"
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)
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log.info(log_msg)
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if self.verbose:
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print(log_msg, flush=True)
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if self.stop_fn and self.stop_fn(self.best_reward):
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stop_fn_flag = True
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return test_stat, stop_fn_flag
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def train_step(self) -> tuple[dict[str, Any], CollectStats, bool]:
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"""Perform one training step."""
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assert self.episode_per_test is not None
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assert self.train_collector is not None
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stop_fn_flag = False
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if self.train_fn:
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self.train_fn(self.epoch, self.env_step)
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result = self.train_collector.collect(
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n_step=self.step_per_collect,
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n_episode=self.episode_per_collect,
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)
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self.env_step += result.n_collected_steps
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if result.n_collected_episodes > 0:
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assert result.returns_stat is not None # for mypy
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assert result.lens_stat is not None # for mypy
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self.last_rew = result.returns_stat.mean
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self.last_len = result.lens_stat.mean
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if self.reward_metric: # TODO: move inside collector
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rew = self.reward_metric(result.returns)
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result.returns = rew
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result.returns_stat = SequenceSummaryStats.from_sequence(rew)
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self.logger.log_train_data(asdict(result), self.env_step)
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data = {
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"env_step": str(self.env_step),
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"rew": f"{self.last_rew:.2f}",
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"len": str(int(self.last_len)),
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"n/ep": str(result.n_collected_episodes),
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"n/st": str(result.n_collected_steps),
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}
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if (
|
|
result.n_collected_episodes > 0
|
|
and self.test_in_train
|
|
and self.stop_fn
|
|
and self.stop_fn(result.returns_stat.mean) # type: ignore
|
|
):
|
|
assert self.test_collector is not None
|
|
test_result = test_episode(
|
|
self.policy,
|
|
self.test_collector,
|
|
self.test_fn,
|
|
self.epoch,
|
|
self.episode_per_test,
|
|
self.logger,
|
|
self.env_step,
|
|
)
|
|
assert test_result.returns_stat is not None # for mypy
|
|
if self.stop_fn(test_result.returns_stat.mean):
|
|
stop_fn_flag = True
|
|
self.best_reward = test_result.returns_stat.mean
|
|
self.best_reward_std = test_result.returns_stat.std
|
|
else:
|
|
self.policy.train()
|
|
return data, result, stop_fn_flag
|
|
|
|
# TODO: move moving average computation and logging into its own logger
|
|
# TODO: maybe think about a command line logger instead of always printing data dict
|
|
def _update_moving_avg_stats_and_log_update_data(self, update_stat: TrainingStats) -> None:
|
|
"""Log losses, update moving average stats, and also modify the smoothed_loss in update_stat."""
|
|
cur_losses_dict = update_stat.get_loss_stats_dict()
|
|
update_stat.smoothed_loss = self._update_moving_avg_stats_and_get_averaged_data(
|
|
cur_losses_dict,
|
|
)
|
|
self.logger.log_update_data(asdict(update_stat), self._gradient_step)
|
|
|
|
# TODO: seems convoluted, there should be a better way of dealing with the moving average stats
|
|
def _update_moving_avg_stats_and_get_averaged_data(
|
|
self,
|
|
data: dict[str, float],
|
|
) -> dict[str, float]:
|
|
"""Add entries to the moving average object in the trainer and retrieve the averaged results.
|
|
|
|
:param data: any entries to be tracked in the moving average object.
|
|
:return: A dictionary containing the averaged values of the tracked entries.
|
|
|
|
"""
|
|
smoothed_data = {}
|
|
for key, loss_item in data.items():
|
|
self.stat[key].add(loss_item)
|
|
smoothed_data[key] = self.stat[key].get()
|
|
return smoothed_data
|
|
|
|
@abstractmethod
|
|
def policy_update_fn(
|
|
self,
|
|
collect_stats: CollectStatsBase,
|
|
) -> TrainingStats:
|
|
"""Policy update function for different trainer implementation.
|
|
|
|
:param collect_stats: provides info about the most recent collection. In the offline case, this will contain
|
|
stats of the whole dataset
|
|
"""
|
|
|
|
def run(self) -> InfoStats:
|
|
"""Consume iterator.
|
|
|
|
See itertools - recipes. Use functions that consume iterators at C speed
|
|
(feed the entire iterator into a zero-length deque).
|
|
"""
|
|
try:
|
|
self.is_run = True
|
|
deque(self, maxlen=0) # feed the entire iterator into a zero-length deque
|
|
info = gather_info(
|
|
start_time=self.start_time,
|
|
policy_update_time=self.policy_update_time,
|
|
gradient_step=self._gradient_step,
|
|
best_reward=self.best_reward,
|
|
best_reward_std=self.best_reward_std,
|
|
train_collector=self.train_collector,
|
|
test_collector=self.test_collector,
|
|
)
|
|
finally:
|
|
self.is_run = False
|
|
|
|
return info
|
|
|
|
def _sample_and_update(self, buffer: ReplayBuffer) -> TrainingStats:
|
|
"""Sample a mini-batch, perform one gradient step, and update the _gradient_step counter."""
|
|
self._gradient_step += 1
|
|
# Note: since sample_size=batch_size, this will perform
|
|
# exactly one gradient step. This is why we don't need to calculate the
|
|
# number of gradient steps, like in the on-policy case.
|
|
update_stat = self.policy.update(sample_size=self.batch_size, buffer=buffer)
|
|
self._update_moving_avg_stats_and_log_update_data(update_stat)
|
|
return update_stat
|
|
|
|
|
|
class OfflineTrainer(BaseTrainer):
|
|
"""Offline trainer, samples mini-batches from buffer and passes them to update.
|
|
|
|
Uses a buffer directly and usually does not have a collector.
|
|
"""
|
|
|
|
# for mypy
|
|
assert isinstance(BaseTrainer.__doc__, str)
|
|
__doc__ += BaseTrainer.gen_doc("offline") + "\n".join(BaseTrainer.__doc__.split("\n")[1:])
|
|
|
|
def policy_update_fn(
|
|
self,
|
|
collect_stats: CollectStatsBase | None = None,
|
|
) -> TrainingStats:
|
|
"""Perform one off-line policy update."""
|
|
assert self.buffer
|
|
update_stat = self._sample_and_update(self.buffer)
|
|
# logging
|
|
self.policy_update_time += update_stat.train_time
|
|
return update_stat
|
|
|
|
|
|
class OffpolicyTrainer(BaseTrainer):
|
|
"""Offpolicy trainer, samples mini-batches from buffer and passes them to update.
|
|
|
|
Note that with this trainer, it is expected that the policy's `learn` method
|
|
does not perform additional mini-batching but just updates params from the received
|
|
mini-batch.
|
|
"""
|
|
|
|
# for mypy
|
|
assert isinstance(BaseTrainer.__doc__, str)
|
|
__doc__ += BaseTrainer.gen_doc("offpolicy") + "\n".join(BaseTrainer.__doc__.split("\n")[1:])
|
|
|
|
def policy_update_fn(
|
|
self,
|
|
# TODO: this is the only implementation where collect_stats is actually needed. Maybe change interface?
|
|
collect_stats: CollectStatsBase,
|
|
) -> TrainingStats:
|
|
"""Perform `update_per_step * n_collected_steps` gradient steps by sampling mini-batches from the buffer.
|
|
|
|
:param collect_stats: the :class:`~TrainingStats` instance returned by the last gradient step. Some values
|
|
in it will be replaced by their moving averages.
|
|
"""
|
|
assert self.train_collector is not None
|
|
n_collected_steps = collect_stats.n_collected_steps
|
|
n_gradient_steps = round(self.update_per_step * n_collected_steps)
|
|
if n_gradient_steps == 0:
|
|
raise ValueError(
|
|
f"n_gradient_steps is 0, n_collected_steps={n_collected_steps}, "
|
|
f"update_per_step={self.update_per_step}",
|
|
)
|
|
for _ in range(n_gradient_steps):
|
|
update_stat = self._sample_and_update(self.train_collector.buffer)
|
|
|
|
# logging
|
|
self.policy_update_time += update_stat.train_time
|
|
# TODO: only the last update_stat is returned, should be improved
|
|
return update_stat
|
|
|
|
|
|
class OnpolicyTrainer(BaseTrainer):
|
|
"""On-policy trainer, passes the entire buffer to .update and resets it after.
|
|
|
|
Note that it is expected that the learn method of a policy will perform
|
|
batching when using this trainer.
|
|
"""
|
|
|
|
# for mypy
|
|
assert isinstance(BaseTrainer.__doc__, str)
|
|
__doc__ = BaseTrainer.gen_doc("onpolicy") + "\n".join(BaseTrainer.__doc__.split("\n")[1:])
|
|
|
|
def policy_update_fn(
|
|
self,
|
|
result: CollectStatsBase | None = None,
|
|
) -> TrainingStats:
|
|
"""Perform one on-policy update by passing the entire buffer to the policy's update method."""
|
|
assert self.train_collector is not None
|
|
training_stat = self.policy.update(
|
|
sample_size=0,
|
|
buffer=self.train_collector.buffer,
|
|
# Note: sample_size is None, so the whole buffer is used for the update.
|
|
# The kwargs are in the end passed to the .learn method, which uses
|
|
# batch_size to iterate through the buffer in mini-batches
|
|
# Off-policy algos typically don't use the batch_size kwarg at all
|
|
batch_size=self.batch_size,
|
|
repeat=self.repeat_per_collect,
|
|
)
|
|
|
|
# just for logging, no functional role
|
|
self.policy_update_time += training_stat.train_time
|
|
# TODO: remove the gradient step counting in trainers? Doesn't seem like
|
|
# it's important and it adds complexity
|
|
self._gradient_step += 1
|
|
if self.batch_size is None:
|
|
self._gradient_step += 1
|
|
elif self.batch_size > 0:
|
|
self._gradient_step += int((len(self.train_collector.buffer) - 0.1) // self.batch_size)
|
|
|
|
# Note: this is the main difference to the off-policy trainer!
|
|
# The second difference is that batches of data are sampled without replacement
|
|
# during training, whereas in off-policy or offline training, the batches are
|
|
# sampled with replacement (and potentially custom prioritization).
|
|
self.train_collector.reset_buffer(keep_statistics=True)
|
|
|
|
# The step is the number of mini-batches used for the update, so essentially
|
|
self._update_moving_avg_stats_and_log_update_data(training_stat)
|
|
|
|
return training_stat
|