TemplateModelComposer

class maze.perception.models.template_model_composer.TemplateModelComposer(action_spaces_dict: Dict[Union[str, int], gym.spaces.Dict], observation_spaces_dict: Dict[Union[str, int], gym.spaces.Dict], distribution_mapper_config: Union[None, str, Mapping[str, Any], Any], model_builder: Union[None, str, Mapping[str, Any], Any, Type[maze.perception.builders.base.BaseModelBuilder]], policy: Union[None, str, Mapping[str, Any], Any], critic: Union[None, str, Mapping[str, Any], Any])

Composes template models from configs.

Parameters

  • action_spaces_dict – Dict of sub-step id to action space.

  • observation_spaces_dict – Dict of sub-step id to observation space.

  • distribution_mapper_config – Distribution mapper configuration.

  • model_builder – The model builder (template) to create the model from.

  • policy – Specifies the policy type as a configType. E.g. {‘type’: maze.perception.models.policies.ProbabilisticPolicyComposer} specifies a probabilistic policy.

  • critic – Specifies the critic type as a configType. E.g. {‘type’: maze.perception.models.critics.StateCriticComposer} specifies the single step state critic.

classmethod check_model_config(model_config: Union[None, str, Mapping[str, Any], Any]) → None

Asserts the provided model config for consistency. :param model_config: The model config to check.

property critic

Implementation of the BaseModelComposer interface, returns the value networks.

property policy

Implementation of the BaseModelComposer interface, returns the policy networks.

template_perception_net(observation_space: gym.spaces.Dict) → maze.perception.blocks.inference.InferenceBlock

Compiles a template perception network for a given observation space.

Parameters

observation_space – The observation space tp build the model for.

Returns

A Perception Inference Block.

template_policy_net(observation_space: gym.spaces.Dict, action_space: gym.spaces.Dict) → maze.perception.blocks.inference.InferenceBlock

Compiles a template policy network.

Parameters

  • observation_space – The input observations for the perception network.

  • action_space – The action space that defines the network action heads.

Returns

A policy network (actor) InferenceBlock.

template_q_value_net(observation_space: Optional[gym.spaces.Dict], action_space: gym.spaces.Dict, only_discrete_spaces: bool, perception_net: Optional[maze.perception.blocks.inference.InferenceBlock] = None) → maze.perception.blocks.inference.InferenceBlock

Compiles a template state action (Q) value network.

Parameters

  • observation_space – The input observations for the perception network.

  • action_space – The action space that defines the network action heads.

  • perception_net – A initial network to continue from. (e.g. useful for shared weights. Model building continues from the key ‘latent’.)

  • only_discrete_spaces – A dict specifying if the action spaces w.r.t. the step only hold discrete action spaces.

Returns

A q value network (critic) InferenceBlock.

template_value_net(observation_space: Optional[gym.spaces.Dict], perception_net: Optional[maze.perception.blocks.inference.InferenceBlock] = None) → maze.perception.blocks.inference.InferenceBlock

Compiles a template value network.

Parameters

  • observation_space – The input observations for the perception network.

  • perception_net – A initial network to continue from. (e.g. useful for shared weights. Model building continues from the key ‘latent’.)

Returns

A value network (critic) InferenceBlock.