config

config#

Source code: tianshou/highlevel/config.py

class TrainingConfig(*, max_epochs: int = 100, epoch_num_steps: int = 30000, num_training_envs: int = -1, num_test_envs: int = 1, test_step_num_episodes: int = -1, buffer_size: int = 4096, collection_step_num_env_steps: int | None = 2048, collection_step_num_episodes: int | None = None, start_timesteps: int = 0, start_timesteps_random: bool = False, replay_buffer_ignore_obs_next: bool = False, replay_buffer_save_only_last_obs: bool = False, replay_buffer_stack_num: int = 1)[source]#

Bases: ToStringMixin

Training configuration.

max_epochs: int = 100#

the (maximum) 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 one test step, where each training step

[for the online case] collects environment steps/transitions (collection step), adding them to the (replay) buffer (see collection_step_num_env_steps and collection_step_num_episodes)

performs an update step via the RL algorithm being used, which can involve one or more actual gradient updates, depending on the algorithm

and the test step collects num_episodes_per_test test episodes in order to evaluate agent performance.

Training may be stopped early if the stop criterion is met (see stop_fn).

For online training, the number of training steps in each epoch is indirectly determined by epoch_num_steps: As many training steps will be performed as are required in order to reach epoch_num_steps total steps in the training environments. Specifically, if the number of transitions collected per step is c (see collection_step_num_env_steps) and epoch_num_steps is set to s, then the number of training steps per epoch is ceil(s / c). Therefore, if max_epochs = e, the total number of environment steps taken during training can be computed as e * ceil(s / c) * c.

For offline training, the number of training steps per epoch is equal to epoch_num_steps.

epoch_num_steps: int = 30000#: For an online algorithm, this is the total number of environment steps to be collected per epoch, and, for an offline algorithm, it is the total number of training steps to take per epoch. See max_epochs for an explanation of epoch semantics.

num_training_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

test_step_num_episodes: int = -1#

the total number of episodes to collect in each test step (across all test environments).

-1 means this will be set to the number of test environments, i.e. each test environment will run exactly one episode per test step.

buffer_size: int = 4096#: the total size of the sample/replay buffer, in which environment steps (transitions) are stored

collection_step_num_env_steps: int | None = 2048#

the number of environment steps/transitions to collect in each collection step before the network update within each training step.

This is mutually exclusive with collection_step_num_episodes, and one of the two must be set.

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 max_epochs for information on the total number of environment steps being collected during training.

collection_step_num_episodes: int | None = None#

the number of episodes to collect in each collection step before the network update within each training step. If this is set, the number of environment steps collected in each collection step is the sum of the lengths of the episodes collected.

This is mutually exclusive with collection_step_num_env_steps, and one of the two must be set.

start_timesteps: int = 0#: the number of environment steps to collect before the actual training loop begins

start_timesteps_random: bool = False#: whether to use a random policy (instead of the initial or restored policy to be trained) when collecting the initial start_timesteps environment steps before training

replay_buffer_ignore_obs_next: bool = False#

whether to ignore the obs_next field in the collected samples when storing them in the buffer and instead use the one-in-the-future of obs as the next observation. This can be useful for very large observations, like for Atari, in order to save RAM.

However, setting this to True may introduce an error at the last steps of episodes! Should only be used in exceptional cases and only when you know what you are doing. Currently only used in Atari examples and may be removed in the future!

replay_buffer_save_only_last_obs: bool = False#

if True, for the case where the environment outputs stacked frames (e.g. because it is using a FrameStack wrapper), save only the most recent frame so as not to duplicate observations in buffer memory. Specifically, if the environment outputs observations obs with shape (N, …), only obs[-1] of shape (…) will be stored. Frame stacking with a fixed number of frames can then be recreated at the buffer level by setting replay_buffer_stack_num.

Note: Currently only used in Atari examples and may be removed in the future!

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).

Note: it is recommended to do this stacking on the environment level by using something like gymnasium’s FrameStack instead. Setting this to larger than one in conjunction with replay_buffer_save_only_last_obs means that stacking will be recreated at the buffer level, which is more memory-efficient.

Currently only used in Atari examples and may be removed in the future!

class OnlineTrainingConfig(*, max_epochs: int = 100, epoch_num_steps: int = 30000, num_training_envs: int = -1, num_test_envs: int = 1, test_step_num_episodes: int = -1, buffer_size: int = 4096, collection_step_num_env_steps: int | None = 2048, collection_step_num_episodes: int | None = None, start_timesteps: int = 0, start_timesteps_random: bool = False, replay_buffer_ignore_obs_next: bool = False, replay_buffer_save_only_last_obs: bool = False, replay_buffer_stack_num: int = 1, test_in_training: bool = False)[source]#

Bases: TrainingConfig

collection_step_num_env_steps: int | None = 2048#

the number of environment steps/transitions to collect in each collection step before the network update within each training step.

This is mutually exclusive with collection_step_num_episodes, and one of the two must be set.

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 max_epochs for information on the total number of environment steps being collected during training.

collection_step_num_episodes: int | None = None#

the number of episodes to collect in each collection step before the network update within each training step. If this is set, the number of environment steps collected in each collection step is the sum of the lengths of the episodes collected.

This is mutually exclusive with collection_step_num_env_steps, and one of the two must be set.

test_in_training: bool = False#: Whether to apply a test step within a training step depending on the early stopping criterion (see with_epoch_stop_callback()) being satisfied based on the data collected within the training step. Specifically, after each collect step, we check whether the early stopping criterion would be satisfied by data we collected (provided that at least one episode was indeed completed, such that we can evaluate returns, etc.). If the criterion is satisfied, we perform a full test step (collecting test_step_num_episodes episodes in order to evaluate performance), and if the early stopping criterion is also satisfied based on the test data, we stop training early.

class OnPolicyTrainingConfig(*, max_epochs: int = 100, epoch_num_steps: int = 30000, num_training_envs: int = -1, num_test_envs: int = 1, test_step_num_episodes: int = -1, buffer_size: int = 4096, collection_step_num_env_steps: int | None = 2048, collection_step_num_episodes: int | None = None, start_timesteps: int = 0, start_timesteps_random: bool = False, replay_buffer_ignore_obs_next: bool = False, replay_buffer_save_only_last_obs: bool = False, replay_buffer_stack_num: int = 1, test_in_training: bool = False, batch_size: int | None = 64, update_step_num_repetitions: int = 1)[source]#

Bases: OnlineTrainingConfig

batch_size: int | None = 64#: Use mini-batches of this size for gradient updates (causing the gradient to be less accurate, a form of regularization). Set batch_size=None for the full buffer that was collected within the training step to be used for the gradient update (no mini-batching).

update_step_num_repetitions: int = 1#: controls, within one update step of an on-policy algorithm, the number of times the full collected data is applied for gradient updates, i.e. if the parameter is 5, then the collected data shall be used five times to update the policy within the same update step.

class OffPolicyTrainingConfig(*, max_epochs: int = 100, epoch_num_steps: int = 30000, num_training_envs: int = -1, num_test_envs: int = 1, test_step_num_episodes: int = -1, buffer_size: int = 4096, collection_step_num_env_steps: int | None = 2048, collection_step_num_episodes: int | None = None, start_timesteps: int = 0, start_timesteps_random: bool = False, replay_buffer_ignore_obs_next: bool = False, replay_buffer_save_only_last_obs: bool = False, replay_buffer_stack_num: int = 1, test_in_training: bool = False, batch_size: int = 64, update_step_num_gradient_steps_per_sample: float = 1.0)[source]#

Bases: OnlineTrainingConfig

batch_size: int = 64#: the the number of environment steps/transitions to sample from the buffer for a gradient update.

update_step_num_gradient_steps_per_sample: float = 1.0#: the number of gradient steps to perform per sample collected (see collection_step_num_env_steps). 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.

config

Contents

config#