Dynamics Module¶
baconian.algo.dynamics.dynamics_model.DynamicsModel¶
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class
baconian.algo.dynamics.dynamics_model.
DynamicsModel
(env_spec: baconian.core.core.EnvSpec, parameters: baconian.core.parameters.Parameters = None, init_state=None, name='dynamics_model', state_input_scaler: baconian.common.data_pre_processing.DataScaler = None, action_input_scaler: baconian.common.data_pre_processing.DataScaler = None, state_output_scaler: baconian.common.data_pre_processing.DataScaler = None)¶ -
INIT_STATUS
= 'CREATED'¶
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STATUS_LIST
= ('CREATED', 'INITED')¶
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__init__
(env_spec: baconian.core.core.EnvSpec, parameters: baconian.core.parameters.Parameters = None, init_state=None, name='dynamics_model', state_input_scaler: baconian.common.data_pre_processing.DataScaler = None, action_input_scaler: baconian.common.data_pre_processing.DataScaler = None, state_output_scaler: baconian.common.data_pre_processing.DataScaler = None)¶ Parameters: - env_spec (EnvSpec) – environment specifications, such as observation space and action space
- parameters (Parameters) – parameters
- init_state (str) – initial state of dymamics model
- name (str) – name of instance, ‘dynamics_model’ by default
- state_input_scaler (DataScaler) – data preprocessing scaler of state input
- action_input_scaler (DataScaler) – data preprocessing scaler of action input
- state_output_scaler (DataScaler) – data preprocessing scaler of state output
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copy_from
(obj) → bool¶ Parameters: obj – object to copy from Returns: True if successful else raise an error Return type: bool
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init
(*args, **kwargs)¶
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make_copy
()¶ Make a copy of parameters and environment specifications.
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reset_state
(state=None)¶ Parameters: state (np.ndarray) – original state Returns: a random sample space in observation space Return type: np.ndarray
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return_as_env
() → baconian.core.core.Env¶ Returns: an environment with this dynamics model Return type: DynamicsEnvWrapper
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step
(action: numpy.ndarray, state=None, allow_clip=False, **kwargs_for_transit)¶ State transition function (only support one sample transition instead of batch data)
Parameters: - action (np.ndarray) – action to be taken
- state (np.ndarray) – current state, if None, will use stored state (saved from last transition)
- allow_clip (bool) – allow clip of observation space, default False
- kwargs_for_transit – extra kwargs for calling the _state_transit, this is typically related to the specific mode you used
Returns: new state after step
Return type: np.ndarray
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Abstraction class for different types of dynamics model:¶
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class
baconian.algo.dynamics.dynamics_model.
LocalDyanmicsModel
(env_spec: baconian.core.core.EnvSpec, parameters: baconian.core.parameters.Parameters = None, init_state=None, name='dynamics_model', state_input_scaler: baconian.common.data_pre_processing.DataScaler = None, action_input_scaler: baconian.common.data_pre_processing.DataScaler = None, state_output_scaler: baconian.common.data_pre_processing.DataScaler = None)¶
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class
baconian.algo.dynamics.dynamics_model.
GlobalDynamicsModel
(env_spec: baconian.core.core.EnvSpec, parameters: baconian.core.parameters.Parameters = None, init_state=None, name='dynamics_model', state_input_scaler: baconian.common.data_pre_processing.DataScaler = None, action_input_scaler: baconian.common.data_pre_processing.DataScaler = None, state_output_scaler: baconian.common.data_pre_processing.DataScaler = None)¶
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class
baconian.algo.dynamics.dynamics_model.
DifferentiableDynamics
(input_node_dict: dict, output_node_dict: dict)¶ -
__init__
(input_node_dict: dict, output_node_dict: dict)¶
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grad_on_input
(key_or_node: (<class 'str'>, <sphinx.ext.autodoc.importer._MockObject object at 0x7f1b52914240>), order=1, batch_flag=False)¶
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split_and_hessian
(out_node, innode)¶
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