schola.scripts.sb3.utils.SingleEnvRewardCallback
class schola.scripts.sb3.utils.SingleEnvRewardCallback(verbose=0, id=0, frequency=10)
: Bases: BaseCallback
Callback for logging rewards and steps taken by a single environment inside a vector environment.
Parameters: : - verbose (int) – Verbosity level.
- id (int) – The id of the environment to log rewards and steps for.
- frequency (int) – The frequency at which to log the rewards and steps taken.
episode_reward : The reward for the current episode.
Type: : float
episode_rewards : The rewards for each episode.
Type: : List[float]
episode_steps : The number of steps taken in the current episode.
Type: : int
step_count : The number of steps taken in each episode.
Type: : List[int]
last_logging_interval : The last interval that was logged.
Type: : int
logging_interval_size : The frequency at which to log the rewards and steps taken.
Type: : int
id : The id of the environment to log rewards and steps for.
Type: : int
Methods
__init__([verbose, id, frequency]) | |
get_reward_interval() | Returns the rewards for the last logging interval. |
get_step_interval() | Returns the steps taken for each episode in the last logging interval. |
increment_logging_interval() | Increments the logging interval by self.logging_interval_size steps. |
init_callback(model) | Initialize the callback by saving references to the RL model and the training environment for convenience. |
on_rollout_end() | |
on_rollout_start() | |
on_step() | This method will be called by the model after each call to env.step(). |
on_training_end() | |
on_training_start(locals_, globals_) | |
update_child_locals(locals_) | Update the references to the local variables on sub callbacks. |
update_locals(locals_) | Update the references to the local variables. |
Attributes
ready_to_log | Returns whether the environment is ready to log, by checking if there are more episodes completed than self.logging_interval_size since we last logged. |
model | |
logger |
__init__(verbose=0, id=0, frequency=10)
get_reward_interval() : Returns the rewards for the last logging interval.
Returns: : The rewards for the last logging interval.
Return type: : List[float]
get_step_interval() : Returns the steps taken for each episode in the last logging interval.
Returns: : The steps taken for each episode in the last logging interval.
Return type: : List[int]
increment_logging_interval() : Increments the logging interval by self.logging_interval_size steps.
Return type: : None
property ready_to_log*: bool* : Returns whether the environment is ready to log, by checking if there are more episodes completed than self.logging_interval_size since we last logged.
Returns: : Whether the environment is ready to log.
Return type: : bool