schola.sb3.env.VecEnv
class schola.sb3.env.VecEnv(unreal_connection, verbosity=0)
: Bases: VecEnv
Methods
__init__(unreal_connection[, verbosity]) | |
close() | Clean up the environment’s resources. |
env_is_wrapped(wrapper_class[, indices]) | Check if environments are wrapped with a given wrapper. |
env_method(*method_args[, indices]) | Call instance methods of vectorized environments. |
get_attr(attr_name[, indices]) | Return attribute from vectorized environment. |
get_images() | Return RGB images from each environment when available |
getattr_depth_check(name, already_found) | Check if an attribute reference is being hidden in a recursive call to __getattr__ |
render([mode]) | Gym environment rendering |
reset() | Reset all the environments and return an array of observations, or a tuple of observation arrays. |
seed([seed]) | Sets the random seeds for all environments, based on a given seed. |
set_attr(attr_name, value[, indices]) | Set attribute inside vectorized environments. |
set_options([options]) | Set the options for the environment. |
step(actions) | Step the environments with the given action |
step_async(actions) | Tell all the environments to start taking a step with the given actions. |
step_wait() | Wait for the step taken with step_async(). |
Attributes
unwrapped |
Parameters: : verbosity (int)
__init__(unreal_connection, verbosity=0) : Parameters: : verbosity (int)
close() : Clean up the environment’s resources.
Return type: : None
env_is_wrapped(wrapper_class, indices=None) : Check if environments are wrapped with a given wrapper.
Parameters: : - method_name – The name of the environment method to invoke.
- indices – Indices of envs whose method to call
- method_args – Any positional arguments to provide in the call
- method_kwargs – Any keyword arguments to provide in the call
Returns: : True if the env is wrapped, False otherwise, for each env queried.
Return type: : bool
env_method(*method_args, indices=None, **method_kwargs) : Call instance methods of vectorized environments.
Parameters: : - method_name – The name of the environment method to invoke.
- indices – Indices of envs whose method to call
- method_args – Any positional arguments to provide in the call
- method_kwargs – Any keyword arguments to provide in the call
Returns: : List of items returned by the environment’s method call
get_attr(attr_name, indices=None) : Return attribute from vectorized environment.
Parameters: : - attr_name – The name of the attribute whose value to return
- indices – Indices of envs to get attribute from
Returns: : List of values of ‘attr_name’ in all environments
reset() : Reset all the environments and return an array of observations, or a tuple of observation arrays.
If step_async is still doing work, that work will be cancelled and step_wait() should not be called until step_async() is invoked again.
Returns: : observation
Return type: : Dict[str, ndarray]
seed(seed=None) : Sets the random seeds for all environments, based on a given seed. Each individual environment will still get its own seed, by incrementing the given seed. WARNING: since gym 0.26, those seeds will only be passed to the environment at the next reset.
Parameters: : seed (int | None) – The random seed. May be None for completely random seeding.
Returns: : Returns a list containing the seeds for each individual env. Note that all list elements may be None, if the env does not return anything when being seeded.
Return type: : None
set_attr(attr_name, value, indices=None) : Set attribute inside vectorized environments.
Parameters: : - attr_name – The name of attribute to assign new value
- value – Value to assign to attr_name
- indices – Indices of envs to assign value
Returns:
set_options(options=None) : Set the options for the environment.
Parameters: : options (Optional*[Dict[str,str]],* optional) – The options to set, by default None.
Return type: : None
step_async(actions) : Tell all the environments to start taking a step with the given actions. Call step_wait() to get the results of the step.
You should not call this if a step_async run is already pending.
Parameters: : actions (List[ndarray] | List[Dict[str, ndarray*]**]*)
Return type: : None
step_wait() : Wait for the step taken with step_async().
Returns: : observation, reward, done, information
Return type: : Tuple[Dict[str, ndarray], ndarray, ndarray, List[Dict[str, str]]]