SamplingConfig: Improve/extend docstrings, clearly explaining the parameters

tianshou/data/buffer/base.py

@@ -23,7 +23,7 @@ class ReplayBuffer:
     :param size: the maximum size of replay buffer.
     :param stack_num: the frame-stack sampling argument, should be greater than or
         equal to 1. Default to 1 (no stacking).
-    :param ignore_obs_next: whether to store obs_next. Default to False.
+    :param ignore_obs_next: whether to not store obs_next. Default to False.
     :param save_only_last_obs: only save the last obs/obs_next when it has a shape
         of (timestep, ...) because of temporal stacking. Default to False.
     :param sample_avail: the parameter indicating sampling only available index

tianshou/highlevel/config.py

@@ -6,29 +6,115 @@ from tianshou.utils.string import ToStringMixin
 
 @dataclass
 class SamplingConfig(ToStringMixin):
-    """Sampling, epochs, parallelization, buffers, collectors, and batching."""
+    """Configuration of sampling, epochs, parallelization, buffers, collectors, and batching."""
 
     # TODO: What are the most reasonable defaults?
     num_epochs: int = 100
+    """
+    the number of epochs to run training for. An epoch is the outermost iteration level and each
+    epoch consists of a number of training steps and a test step, where each training step
+
+      * collects environment steps/transitions (collection step), adding them to the (replay)
+        buffer (see :attr:`step_per_collect`)
+      * performs one or more gradient updates (see :attr:`update_per_step`).
+
+    The number of training steps in each epoch is indirectly determined by
+    :attr:`step_per_epoch`: As many training steps will be performed as are required in
+    order to reach :attr:`step_per_epoch` total steps in the training environments.
+    Specifically, if the number of transitions collected per step is `c` (see
+    :attr:`step_per_collect`) and :attr:`step_per_epoch` is set to `s`, then the number
+    of training steps per epoch is `ceil(s / c)`.
+
+    Therefore, if `num_epochs = e`, the total number of environment steps taken during training
+    can be computed as `e * ceil(s / c) * c`.
+    """
+
     step_per_epoch: int = 30000
+    """
+    the total number of environment steps to be made per epoch. See :attr:`num_epochs` for
+    an explanation of epoch semantics.
+    """
+
     batch_size: int = 64
+    """for off-policy algorithms, this is the number of environment steps/transitions to sample
+    from the buffer for a gradient update; for on-policy algorithms, its use is algorithm-specific.
+    On-policy algorithms use the full buffer that was collected in the preceding collection step
+    but they may use this parameter to perform the gradient update using mini-batches of this size
+    (causing the gradient to be less accurate, a form of regularization).
+    """
+
     num_train_envs: int = -1
     """the number of training environments to use. If set to -1, use number of CPUs/threads."""
+
     num_test_envs: int = 1
+    """the number of test environments to use"""
+
     buffer_size: int = 4096
+    """the total size of the sample/replay buffer, in which environment steps (transitions) are
+    stored"""
+
     step_per_collect: int = 2048
+    """
+    the number of environment steps/transitions to collect in each collection step before the
+    network update within each training step.
+    Note that the exact number can be reached only if this is a multiple of the number of
+    training environments being used, as each training environment will produce the same
+    (non-zero) number of transitions.
+    Specifically, if this is set to `n` and `m` training environments are used, then the total
+    number of transitions collected per collection step is `ceil(n / m) * m =: c`.
+
+    See :attr:`num_epochs` for information on the total number of environment steps being
+    collected during training.
+    """
+
     repeat_per_collect: int | None = 10
+    """
+    controls, within one gradient update step of an on-policy algorithm, the number of times an
+    actual gradient update is applied using the full collected dataset, i.e. if the parameter is
+    `n`, then the collected data shall be used five times to update the policy within the same
+    training step.
+
+    The parameter is ignored and may be set to None for off-policy and offline algorithms.
+    """
+
     update_per_step: float = 1.0
     """
-    Only used in off-policy algorithms.
-    How many gradient steps to perform per step in the environment (i.e., per sample added to the buffer).
+    for off-policy algorithms only: the number of gradient steps to perform per sample
+    collected (see :attr:`step_per_collect`).
+    Specifically, if this is set to `u` and the number of samples collected in the preceding
+    collection step is `n`, then `round(u * n)` gradient steps will be performed.
+
+    Note that for on-policy algorithms, only a single gradient update is usually performed,
+    because thereafter, the samples no longer reflect the behavior of the updated policy.
+    To change the number of gradient updates for an on-policy algorithm, use parameter
+    :attr:`repeat_per_collect` instead.
     """
+
     start_timesteps: int = 0
+    """
+    the number of environment steps to collect before the actual training loop begins
+    """
+
     start_timesteps_random: bool = False
-    # TODO can we set the parameters below intelligently? Perhaps based on env. representation?
+    """
+    whether to use a random policy (instead of the initial or restored policy to be trained)
+    when collecting the initial :attr:`start_timesteps` environment steps before training
+    """
+
     replay_buffer_ignore_obs_next: bool = False
+
     replay_buffer_save_only_last_obs: bool = False
+    """if True, only the most recent frame is saved when appending to experiences rather than the
+    full stacked frames. This avoids duplicating observations in buffer memory. Set to False to
+    save stacked frames in full.
+    """
+
     replay_buffer_stack_num: int = 1
+    """
+    the number of consecutive environment observations to stack and use as the observation input
+    to the agent for each time step. Setting this to a value greater than 1 can help agents learn
+    temporal aspects (e.g. velocities of moving objects for which only positions are observed).
+    """
 
     def __post_init__(self) -> None:
         if self.num_train_envs == -1: