3592f45446
- Fixes an inconsistency in the implementation of Discrete CRR. Now it uses `Critic` class for its critic, following conventions in other actor-critic policies; - Updates several offline policies to use `ActorCritic` class for its optimizer to eliminate randomness caused by parameter sharing between actor and critic; - Add `writer.flush()` in TensorboardLogger to ensure real-time result; - Enable `test_collector=None` in 3 trainers to turn off testing during training; - Updates the Atari offline results in README.md; - Moves Atari offline RL examples to `examples/offline`; tests to `test/offline` per review comments.
88 lines
3.2 KiB
Python
88 lines
3.2 KiB
Python
import warnings
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from typing import Any, Callable, Optional, Tuple
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from tensorboard.backend.event_processing import event_accumulator
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from torch.utils.tensorboard import SummaryWriter
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from tianshou.utils.logger.base import LOG_DATA_TYPE, BaseLogger
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class TensorboardLogger(BaseLogger):
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"""A logger that relies on tensorboard SummaryWriter by default to visualize \
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and log statistics.
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:param SummaryWriter writer: the writer to log data.
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:param int train_interval: the log interval in log_train_data(). Default to 1000.
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:param int test_interval: the log interval in log_test_data(). Default to 1.
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:param int update_interval: the log interval in log_update_data(). Default to 1000.
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:param int save_interval: the save interval in save_data(). Default to 1 (save at
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the end of each epoch).
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"""
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def __init__(
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self,
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writer: SummaryWriter,
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train_interval: int = 1000,
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test_interval: int = 1,
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update_interval: int = 1000,
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save_interval: int = 1,
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) -> None:
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super().__init__(train_interval, test_interval, update_interval)
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self.save_interval = save_interval
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self.last_save_step = -1
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self.writer = writer
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def write(self, step_type: str, step: int, data: LOG_DATA_TYPE) -> None:
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for k, v in data.items():
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self.writer.add_scalar(k, v, global_step=step)
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self.writer.flush() # issue #482
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def save_data(
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self,
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epoch: int,
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env_step: int,
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gradient_step: int,
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save_checkpoint_fn: Optional[Callable[[int, int, int], None]] = None,
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) -> None:
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if save_checkpoint_fn and epoch - self.last_save_step >= self.save_interval:
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self.last_save_step = epoch
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save_checkpoint_fn(epoch, env_step, gradient_step)
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self.write("save/epoch", epoch, {"save/epoch": epoch})
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self.write("save/env_step", env_step, {"save/env_step": env_step})
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self.write(
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"save/gradient_step", gradient_step,
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{"save/gradient_step": gradient_step}
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)
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def restore_data(self) -> Tuple[int, int, int]:
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ea = event_accumulator.EventAccumulator(self.writer.log_dir)
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ea.Reload()
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try: # epoch / gradient_step
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epoch = ea.scalars.Items("save/epoch")[-1].step
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self.last_save_step = self.last_log_test_step = epoch
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gradient_step = ea.scalars.Items("save/gradient_step")[-1].step
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self.last_log_update_step = gradient_step
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except KeyError:
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epoch, gradient_step = 0, 0
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try: # offline trainer doesn't have env_step
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env_step = ea.scalars.Items("save/env_step")[-1].step
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self.last_log_train_step = env_step
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except KeyError:
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env_step = 0
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return epoch, env_step, gradient_step
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class BasicLogger(TensorboardLogger):
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"""BasicLogger has changed its name to TensorboardLogger in #427.
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This class is for compatibility.
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"""
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def __init__(self, *args: Any, **kwargs: Any) -> None:
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warnings.warn(
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"Deprecated soon: BasicLogger has renamed to TensorboardLogger in #427."
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)
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super().__init__(*args, **kwargs)
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