122 lines
5.3 KiB
Python
122 lines
5.3 KiB
Python
import multiprocessing
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from dataclasses import dataclass
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from tianshou.utils.string import ToStringMixin
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@dataclass
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class SamplingConfig(ToStringMixin):
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"""Configuration of sampling, epochs, parallelization, buffers, collectors, and batching."""
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# TODO: What are the most reasonable defaults?
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num_epochs: int = 100
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"""
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the number of epochs to run training for. An epoch is the outermost iteration level and each
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epoch consists of a number of training steps and a test step, where each training step
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* collects environment steps/transitions (collection step), adding them to the (replay)
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buffer (see :attr:`step_per_collect`)
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* performs one or more gradient updates (see :attr:`update_per_step`).
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The number of training steps in each epoch is indirectly determined by
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:attr:`step_per_epoch`: As many training steps will be performed as are required in
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order to reach :attr:`step_per_epoch` total steps in the training environments.
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Specifically, if the number of transitions collected per step is `c` (see
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:attr:`step_per_collect`) and :attr:`step_per_epoch` is set to `s`, then the number
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of training steps per epoch is `ceil(s / c)`.
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Therefore, if `num_epochs = e`, the total number of environment steps taken during training
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can be computed as `e * ceil(s / c) * c`.
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"""
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step_per_epoch: int = 30000
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"""
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the total number of environment steps to be made per epoch. See :attr:`num_epochs` for
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an explanation of epoch semantics.
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"""
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batch_size: int = 64
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"""for off-policy algorithms, this is the number of environment steps/transitions to sample
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from the buffer for a gradient update; for on-policy algorithms, its use is algorithm-specific.
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On-policy algorithms use the full buffer that was collected in the preceding collection step
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but they may use this parameter to perform the gradient update using mini-batches of this size
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(causing the gradient to be less accurate, a form of regularization).
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"""
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num_train_envs: int = -1
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"""the number of training environments to use. If set to -1, use number of CPUs/threads."""
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num_test_envs: int = 1
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"""the number of test environments to use"""
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buffer_size: int = 4096
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"""the total size of the sample/replay buffer, in which environment steps (transitions) are
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stored"""
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step_per_collect: int = 2048
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"""
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the number of environment steps/transitions to collect in each collection step before the
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network update within each training step.
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Note that the exact number can be reached only if this is a multiple of the number of
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training environments being used, as each training environment will produce the same
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(non-zero) number of transitions.
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Specifically, if this is set to `n` and `m` training environments are used, then the total
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number of transitions collected per collection step is `ceil(n / m) * m =: c`.
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See :attr:`num_epochs` for information on the total number of environment steps being
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collected during training.
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"""
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repeat_per_collect: int | None = 1
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"""
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controls, within one gradient update step of an on-policy algorithm, the number of times an
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actual gradient update is applied using the full collected dataset, i.e. if the parameter is
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`n`, then the collected data shall be used five times to update the policy within the same
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training step.
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The parameter is ignored and may be set to None for off-policy and offline algorithms.
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"""
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update_per_step: float = 1.0
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"""
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for off-policy algorithms only: the number of gradient steps to perform per sample
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collected (see :attr:`step_per_collect`).
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Specifically, if this is set to `u` and the number of samples collected in the preceding
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collection step is `n`, then `round(u * n)` gradient steps will be performed.
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Note that for on-policy algorithms, only a single gradient update is usually performed,
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because thereafter, the samples no longer reflect the behavior of the updated policy.
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To change the number of gradient updates for an on-policy algorithm, use parameter
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:attr:`repeat_per_collect` instead.
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"""
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start_timesteps: int = 0
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"""
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the number of environment steps to collect before the actual training loop begins
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"""
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start_timesteps_random: bool = False
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"""
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whether to use a random policy (instead of the initial or restored policy to be trained)
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when collecting the initial :attr:`start_timesteps` environment steps before training
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"""
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replay_buffer_ignore_obs_next: bool = False
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replay_buffer_save_only_last_obs: bool = False
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"""if True, only the most recent frame is saved when appending to experiences rather than the
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full stacked frames. This avoids duplicating observations in buffer memory. Set to False to
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save stacked frames in full.
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"""
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replay_buffer_stack_num: int = 1
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"""
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the number of consecutive environment observations to stack and use as the observation input
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to the agent for each time step. Setting this to a value greater than 1 can help agents learn
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temporal aspects (e.g. velocities of moving objects for which only positions are observed).
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"""
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def __post_init__(self) -> None:
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if self.num_train_envs == -1:
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self.num_train_envs = multiprocessing.cpu_count()
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