BaseModelBuilder¶
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class
maze.perception.builders.base.BaseModelBuilder(modality_config: Dict[str, Union[str, Dict[str, Any]]], observation_modality_mapping: Dict[str, str], shared_embedding_keys: Optional[Union[List[str], Dict[str, List[str]]]])¶ Base class for perception default model builders.
- Parameters
modality_config – dictionary mapping perception modalities to blocks and block config parameters.
observation_modality_mapping – A mapping of observation keys to perception modalities.
shared_embedding_keys – The shared embedding keys to use as an input to the critic network where the value can be one of the following: - None, empty list or dict of empty lists: No shared embeddings are used. - A list of str values: The shared embedding keys to use for creating the critic network in each substep (the same keys). - A dict of lists: Here the keys have to refer to the step-keys of the environment, the corresponding lists specify the input keys to the critic network in this step.
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abstract
from_observation_space(observation_space: gym.spaces.Dict) → maze.perception.blocks.inference.InferenceBlock¶ Compiles an inference graph for a given observation space.
Only observations which are contained in the self.observation_modalities dictionary are considered.
- Parameters
observation_space – The respective observation space.
- Returns
the resulting inference block.
Init the shared embedding keys as a dict using the step_keys of the environment.
- Parameters
step_keys – the step keys of the environment steps
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classmethod
to_recurrent_gym_space(observation_space: gym.spaces.Dict, rnn_steps: int) → gym.spaces.Dict¶ Converts the given observation space to a recurrent space.
- Parameters
observation_space – The respective observation space.
rnn_steps – Number of recurrent time steps.
- Returns
The rnn modified dictionary observation space.