agent

`Agent`

A generic agent class.

It is meant to define the general structure for an agent meant to evolve in a environment of olfactory cues. To define such agent, a set of methods need to be implemented. This methods can be seperated into 3 categories:

Training methods
Simulation methods
General methods

The training methods are meant to train the agent before testing their performance in a simulation. A single method is needed for this:

train()

The simulation methods are meant for the agent to make choices and receiving observations during a simulation. The following methods are required for this:

initialize_state(): This method is meant for the state of the agent(s) to be initialized before the simulation loop starts. The state of the agent can be an internal clock, a belief or something else arbitrary.
choose_action(): Here the agent(s) is asked to choose an action to play based on its internal state.
update_state(): Then, after the agent(s) has taken an action, the observation it makes along with whether he reached the source or not is returned to him using this method. This allows the agent to update its internal state.
kill(): Finally, the method asks for a set of agents to be terminated. The basic case happens when the agent reaches the source but it can also be asked to terminate if it has reached the end of the simulation without success.

The general methods are methods to perform general actions with the agent. These methods are:

save(): To save the agent to long term storage.
load(): To load the agent from long term storage.
modify_environment(): To provide an equivalent agent with a different environment linked to it. If the agent has previously been trained, the trained components needs to be adapted to this new environment.
to_gpu(): To create an alternative version of the agent whether the array instances are stored on the GPU memory instead of the CPU memory.
to_cpu(): To create an alternative version of the agent whether the array instances are stored on the CPU memory instead of the GPU memory.

For a user to implement an agent, the main methods to define are the Simulation methods! The training method is, as stated, optional, as some agent definitions do not require it. And the General methods all have some default behavior and are therefore only needed to be overwritten in specific cases.

Parameters:

Name	Type	Description	Default
`environment`	`Environment`	The olfactory environment the agent is meant to evolve in.	required
`threshold`	`float or list[float]`	The olfactory threshold. If an odor cue above this threshold is detected, the agent detects it, else it does not. If a list of threshold is provided, he agent should be able to detect \|thresholds\|+1 levels of odor.	`3e-6`
`actions`	`dict or ndarray`	The set of action available to the agent. It should match the type of environment (ie: if the environment has layers, it should contain a layer component to the action vector, and similarly for a third dimension). Else, a dict of strings and action vectors where the strings represent the action labels. If none is provided, by default, all unit movement vectors are included and shuch for all layers (if the environment has layers.)	`None`
`name`	`str`	A custom name for the agent. If it is not provided it will be named like "-thresh_".	`None`
`seed`	`int`	For reproducible randomness.	`12131415`

Attributes:

Name	Type	Description
`environment`	`Environment`
`threshold`	`float or list[float]`
`name`	`str`
`action_set`	`ndarray`	The actions allowed of the agent. Formulated as movement vectors as [(layer,) (dz,) dy, dx].
`action_labels`	`list[str]`	The labels associated to the action vectors present in the action set.
`saved_at`	`str`	If the agent has been saved, the path at which it is saved is recorded in this variable.
`on_gpu`	`bool`	Whether the arrays are on the GPU memory or not. For this, the support for Cupy needs to be enabled and the agent needs to have been moved to the GPU using the to_gpu() function.
`class_name`	`str`	The name of the class of the agent.
`seed`	`int`	The seed used for the random operations (to allow for reproducability).
`rnd_state`	`RandomState`	The random state variable used to generate random values.

Source code in olfactory_navigation/agent.py

class Agent:
    '''
    A generic agent class.

    It is meant to define the general structure for an agent meant to evolve in a environment of olfactory cues.
    To define such agent, a set of methods need to be implemented. This methods can be seperated into 3 categories:

    1. Training methods
    2. Simulation methods
    3. General methods

    The training methods are meant to train the agent before testing their performance in a simulation. A single method is needed for this:

    - train()

    The simulation methods are meant for the agent to make choices and receiving observations during a simulation. The following methods are required for this:

    - initialize_state(): This method is meant for the state of the agent(s) to be initialized before the simulation loop starts. The state of the agent can be an internal clock, a belief or something else arbitrary.
    - choose_action(): Here the agent(s) is asked to choose an action to play based on its internal state.
    - update_state(): Then, after the agent(s) has taken an action, the observation it makes along with whether he reached the source or not is returned to him using this method. This allows the agent to update its internal state.
    - kill(): Finally, the method asks for a set of agents to be terminated. The basic case happens when the agent reaches the source but it can also be asked to terminate if it has reached the end of the simulation without success.

    The general methods are methods to perform general actions with the agent. These methods are:

    - save(): To save the agent to long term storage.
    - load(): To load the agent from long term storage.
    - modify_environment(): To provide an equivalent agent with a different environment linked to it. If the agent has previously been trained, the trained components needs to be adapted to this new environment.
    - to_gpu(): To create an alternative version of the agent whether the array instances are stored on the GPU memory instead of the CPU memory.
    - to_cpu(): To create an alternative version of the agent whether the array instances are stored on the CPU memory instead of the GPU memory.

    For a user to implement an agent, the main methods to define are the Simulation methods! The training method is, as stated, optional, as some agent definitions do not require it.
    And the General methods all have some default behavior and are therefore only needed to be overwritten in specific cases.


    Parameters
    ----------
    environment : Environment
        The olfactory environment the agent is meant to evolve in.
    threshold : float or list[float], default=3e-6
        The olfactory threshold. If an odor cue above this threshold is detected, the agent detects it, else it does not.
        If a list of threshold is provided, he agent should be able to detect |thresholds|+1 levels of odor.
    actions : dict or np.ndarray, optional
        The set of action available to the agent. It should match the type of environment (ie: if the environment has layers, it should contain a layer component to the action vector, and similarly for a third dimension).
        Else, a dict of strings and action vectors where the strings represent the action labels.
        If none is provided, by default, all unit movement vectors are included and shuch for all layers (if the environment has layers.)
    name : str, optional
        A custom name for the agent. If it is not provided it will be named like "<class_name>-thresh_<threshold>".
    seed : int, default=12131415
        For reproducible randomness.

    Attributes
    ----------
    environment : Environment
    threshold : float or list[float]
    name : str
    action_set : np.ndarray
        The actions allowed of the agent. Formulated as movement vectors as [(layer,) (dz,) dy, dx].
    action_labels : list[str]
        The labels associated to the action vectors present in the action set.
    saved_at : str
        If the agent has been saved, the path at which it is saved is recorded in this variable.
    on_gpu : bool
        Whether the arrays are on the GPU memory or not. For this, the support for Cupy needs to be enabled and the agent needs to have been moved to the GPU using the to_gpu() function.
    class_name : str
        The name of the class of the agent.
    seed : int
        The seed used for the random operations (to allow for reproducability).
    rnd_state : np.random.RandomState
        The random state variable used to generate random values.
    '''
    def __init__(self,
                 environment: Environment,
                 threshold: float | list[float] = 3e-6,
                 actions: dict[str, np.ndarray] | np.ndarray | None = None,
                 name: str | None = None,
                 seed: int = 12131415
                 ) -> None:
        self.environment = environment
        self.threshold = threshold

        # Ensuring thresholds are sorted (if it is a list)
        if isinstance(self.threshold, list):
            self.threshold = sorted(self.threshold)

        # Allowed actions
        self.action_labels = None
        if actions is None:
            if environment.dimensions == 2:
                self.action_set = np.array([
                    [-1,  0], # North
                    [ 0,  1], # East
                    [ 1,  0], # South
                    [ 0, -1]  # West
                ])
                self.action_labels = [
                    'North',
                    'East',
                    'South',
                    'West'
                ]
            elif environment.dimensions == 3:
                self.action_set = np.array([
                    [ 0, -1,  0], # North
                    [ 0,  0,  1], # East
                    [ 0,  1,  0], # South
                    [ 0,  0, -1], # West
                    [ 1,  0,  0], # Up
                    [-1,  0,  0]  # Down
                ])
                self.action_labels = [
                    'North',
                    'East',
                    'South',
                    'West',
                    'Up',
                    'Down'
                ]
            else: # ND
                self.action_set = np.zeros((2*environment.dimensions, environment.dimensions))
                self.action_labels = []
                for dim in range(environment.dimensions):
                    # Increase in dimension 'dim'
                    self.action_set[dim*2, -dim-1] = 1
                    self.action_labels.append(f'd{dim}+1')

                    # Decrease in dimension 'dim'
                    self.action_set[(dim*2) + 1, -dim-1] = -1
                    self.action_labels.append(f'd{dim}-1')

            # Layered
            if environment.has_layers:
                self.action_set = np.array([[layer, *action_vector] for layer in environment.layers for action_vector in self.action_set])
                self.action_labels = [f'l_{layer}_{action}' for  layer in environment.layer_labels for action in self.action_labels]

        # Actions provided as numpy array
        elif isinstance(actions, np.ndarray):
            self.action_set = actions
            self.action_labels = ['a_' + '_'.join([str(dim_a) for dim_a in action_vector]) for action_vector in self.action_set]

        # Actions provided as dict
        else:
            self.action_set = np.ndarray(list(actions.values()))
            self.action_labels = list(actions.keys())

        # Asertion that the shape of the actions set if right
        layered = 0 if not environment.has_layers else 1
        assert self.action_set.shape[1] == (layered + environment.dimensions), f"The shape of the action_set provided is not right. (Found {self.action_set.shape}; expected (., {layered + environment.dimensions}))"

        # setup name
        if name is None:
            self.name = self.class_name
            self.name += f'-tresh_' + (str(self.threshold) if not isinstance(self.threshold, list) else '_'.join(str(t) for t in self.threshold))
        else:
            self.name = name

        # Other variables
        self.saved_at = None

        self.on_gpu = False
        self._alternate_version = None

        # random state
        self.seed = seed
        self.rnd_state = np.random.RandomState(seed = seed)


    @property
    def class_name(self):
        '''
        The name of the class of the agent.
        '''
        return self.__class__.__name__


    # ----------------
    # Training methods
    # ----------------
    def train(self) -> None:
        '''
        Optional function to train the agent in the olfactory environment it is in.
        This function is optional as some agents have some fixed behavior and therefore dont require training.
        '''
        raise NotImplementedError('The train function is not implemented, make an agent subclass to implement the method')


    # ------------------
    # Simulation methods
    # ------------------
    def initialize_state(self,
                         n: int = 1
                         ) -> None:
        '''
        Function to initialize the internal state of the agent(s) for the simulation process. The internal state can be concepts such as the "memory" or "belief" of the agent.
        The n parameter corresponds to how many "instances" need to instanciated. This is meant so that we work with a "group" of agents instead of individual instances.

        This is done with the purpose that the state of the group of agents be stored in (Numpy) arrays to allow vectorization instead of sequential loops.

        Parameters
        ----------
        n : int, default=1
            How many agents to initialize.
        '''
        raise NotImplementedError('The initialize_state function is not implemented, make an agent subclass to implement the method')


    def choose_action(self) -> np.ndarray:
        '''
        Function to allow for the agent(s) to choose an action to take based on its current state.

        It should return a 2D array of shape n by 2 (or 3, or 4 depending of whether the environment has layers and/or a 3rd dimension),
        where n is how many agents are to choose an action. It should be n 2D vectors of (the layer and) the change in the (z,) y, and x positions.

        Returns
        -------
        movement_vector : np.ndarray
            An array of n vectors in 2D space of the movement(s) the agent(s) will take.
        '''
        raise NotImplementedError('The choose_action function is not implemented, make an agent subclass to implement the method')


    def update_state(self,
                     observation: np.ndarray,
                     source_reached: np.ndarray
                     ) -> None | np.ndarray:
        '''
        Function to update the internal state(s) of the agent(s) based on the action(s) taken and the observation(s) received.
        The observations are then compared with the threshold to decide whether something was sensed or not.

        Parameters
        ----------
        observation : np.ndarray
            A 1D array of odor cues (float values) retrieved from the environment.
        source_reached : np.array
            A 1D array of boolean values signifying whether each agent reached or not the source.

        Returns
        -------
        update_successfull : np.ndarray, optional
            If nothing is returned, it means all the agent's state updates have been successfull.
            Else, a boolean np.ndarray of size n can be returned confirming for each agent whether the update has been successful or not.
        '''
        raise NotImplementedError('The update_state function is not implemented, make an agent subclass to implement the method')


    def kill(self,
             simulations_to_kill: np.ndarray
             ) -> None:
        '''
        Function to kill any agents that either reached the source or failed by not reaching the source before the horizon or failing to update its own state.
        The agents where the simulations_to_kill paramater is True have to removed from the list of agents.
        It is necessary because their reference will also be removed from the simulation loop. Therefore, if they are not removed, the array sizes will not match anymore.

        Parameters
        ----------
        simulations_to_kill : np.ndarray
            An array of size n containing boolean values of whether or not agent's simulations are terminated and therefore should be removed.
        '''
        raise NotImplementedError('The kill function is not implemented, make an agent subclass to implement the method')


    # ---------------
    # General methods
    # ---------------
    def save(self,
             folder: str | None = None,
             force: bool = False,
             save_environment: bool = False
             ) -> None:
        '''
        Function to save a trained agent to long term storage.
        By default, the agent is saved in its entirety using pickle.

        However, it is strongly advised to overwrite this method to only save save the necessary components of the agents in order to be able to load it and reproduce its behavior.
        For instance, if the agent is saved after the simulation is run, the state would also be saved within the pickle which is not wanted.

        Parameters
        ----------
        folder : str, optional
            The folder in which the agent's data should be saved.
        force : bool, default=False
            If the agent is already saved at the folder provided, the saving should fail.
            If the already saved agent should be overwritten, this parameter should be toggled to True.
        save_environment : bool, default=False
            Whether to save the agent's linked environment alongside the agent itself.
        '''
        if self.on_gpu:
            cpu_agent = self.to_cpu()
            cpu_agent.save(folder=folder, force=force, save_environment=save_environment)
            return

        # Adding env name to folder path
        if folder is None:
            folder = f'./Agent-{self.name}'
        else:
            folder += f'/Agent-{self.name}'

        # Checking the folder exists or creates it
        if not os.path.exists(folder):
            os.mkdir(folder)
        elif len(os.listdir(folder)) > 0:
            if force:
                shutil.rmtree(folder)
                os.mkdir(folder)
            else:
                raise Exception(f'{folder} is not empty. If you want to overwrite the saved agent, enable "force".')

        # Send self to pickle
        with open(folder + '/binary.pkl', 'wb') as f:
            pickle.dump(self, f)

        # Save environment in folder too if requested
        if save_environment:
            self.environment.save(folder=(folder + f'/Env-{self.environment.name}'))


    @classmethod
    def load(cls,
             folder: str
             ) -> 'Agent':
        '''
        Function to load a trained agent from long term storage.
        By default, as for the save function, it will load the agent from the folder assuming it is a pickle file.

        Parameters
        ----------
        folder : str
            The folder in which the agent was saved.

        Returns
        -------
        loaded_agent : Agent
            The agent loaded from the folder.
        '''
        from olfactory_navigation import agents

        for name, obj in inspect.getmembers(agents):
            if inspect.isclass(obj) and (name in folder) and issubclass(obj, cls) and (obj != cls):
                return obj.load(folder)

        # Default loading with pickle
        with open(folder + '/binary.pkl', 'rb') as f:
            return pickle.load(f)


    def modify_environment(self,
                           new_environment: Environment
                           ) -> 'Agent':
        '''
        Function to modify the environment of the agent.

        Note: By default, a new agent is created with the same threshold and name but with a this new environment!
        If there are any trained elements to the agent, they are to be modified in this method to be adapted to this new environment.

        Parameters
        ----------
        new_environment : Environment
            The new environment to replace the agent in an equivalent agent.

        Returns
        -------
        modified_agent : Agent
            A new Agent whose environment has been replaced.
        '''
        modified_agent = self.__class__(environment=new_environment,
                                        threshold=self.threshold,
                                        name=self.name)
        return modified_agent


    def to_gpu(self) -> 'Agent':
        '''
        Function to send the numpy arrays of the agent to the gpu.
        It returns a new instance of the Agent class with the arrays on the gpu.

        Returns
        -------
        gpu_agent : Agent
            A new environment instance where the arrays are on the gpu memory.
        '''
        assert gpu_support, "GPU support is not enabled, Cupy might need to be installed..."

        # Generating a new instance
        cls = self.__class__
        gpu_agent = cls.__new__(cls)

        # Copying arguments to gpu
        for arg, val in self.__dict__.items():
            if isinstance(val, np.ndarray):
                setattr(gpu_agent, arg, cp.array(val))
            elif arg == 'rnd_state':
                setattr(gpu_agent, arg, cp.random.RandomState(self.seed))
            else:
                setattr(gpu_agent, arg, val)

        # Self reference instances
        self._alternate_version = gpu_agent
        gpu_agent._alternate_version = self

        gpu_agent.on_gpu = True
        return gpu_agent


    def to_cpu(self) -> 'Agent':
        '''
        Function to send the numpy arrays of the agent to the gpu.
        It returns a new instance of the Agent class with the arrays on the gpu.

        Returns
        -------
        cpu_agent : Agent
            A new environment instance where the arrays are on the cpu memory.
        '''
        if self.on_gpu:
            assert self._alternate_version is not None, "Something went wrong"
            return self._alternate_version

        return self

`class_name` `property`

The name of the class of the agent.

`choose_action()`

Function to allow for the agent(s) to choose an action to take based on its current state.

It should return a 2D array of shape n by 2 (or 3, or 4 depending of whether the environment has layers and/or a 3rd dimension), where n is how many agents are to choose an action. It should be n 2D vectors of (the layer and) the change in the (z,) y, and x positions.

Returns:

Name	Type	Description
`movement_vector`	`ndarray`	An array of n vectors in 2D space of the movement(s) the agent(s) will take.

Source code in olfactory_navigation/agent.py

def choose_action(self) -> np.ndarray:
    '''
    Function to allow for the agent(s) to choose an action to take based on its current state.

    It should return a 2D array of shape n by 2 (or 3, or 4 depending of whether the environment has layers and/or a 3rd dimension),
    where n is how many agents are to choose an action. It should be n 2D vectors of (the layer and) the change in the (z,) y, and x positions.

    Returns
    -------
    movement_vector : np.ndarray
        An array of n vectors in 2D space of the movement(s) the agent(s) will take.
    '''
    raise NotImplementedError('The choose_action function is not implemented, make an agent subclass to implement the method')

`initialize_state(n=1)`

Function to initialize the internal state of the agent(s) for the simulation process. The internal state can be concepts such as the "memory" or "belief" of the agent. The n parameter corresponds to how many "instances" need to instanciated. This is meant so that we work with a "group" of agents instead of individual instances.

This is done with the purpose that the state of the group of agents be stored in (Numpy) arrays to allow vectorization instead of sequential loops.

Parameters:

Name	Type	Description	Default
`n`	`int`	How many agents to initialize.	`1`

Source code in olfactory_navigation/agent.py

def initialize_state(self,
                     n: int = 1
                     ) -> None:
    '''
    Function to initialize the internal state of the agent(s) for the simulation process. The internal state can be concepts such as the "memory" or "belief" of the agent.
    The n parameter corresponds to how many "instances" need to instanciated. This is meant so that we work with a "group" of agents instead of individual instances.

    This is done with the purpose that the state of the group of agents be stored in (Numpy) arrays to allow vectorization instead of sequential loops.

    Parameters
    ----------
    n : int, default=1
        How many agents to initialize.
    '''
    raise NotImplementedError('The initialize_state function is not implemented, make an agent subclass to implement the method')

`kill(simulations_to_kill)`

Function to kill any agents that either reached the source or failed by not reaching the source before the horizon or failing to update its own state. The agents where the simulations_to_kill paramater is True have to removed from the list of agents. It is necessary because their reference will also be removed from the simulation loop. Therefore, if they are not removed, the array sizes will not match anymore.

Parameters:

Name	Type	Description	Default
`simulations_to_kill`	`ndarray`	An array of size n containing boolean values of whether or not agent's simulations are terminated and therefore should be removed.	required

Source code in olfactory_navigation/agent.py

def kill(self,
         simulations_to_kill: np.ndarray
         ) -> None:
    '''
    Function to kill any agents that either reached the source or failed by not reaching the source before the horizon or failing to update its own state.
    The agents where the simulations_to_kill paramater is True have to removed from the list of agents.
    It is necessary because their reference will also be removed from the simulation loop. Therefore, if they are not removed, the array sizes will not match anymore.

    Parameters
    ----------
    simulations_to_kill : np.ndarray
        An array of size n containing boolean values of whether or not agent's simulations are terminated and therefore should be removed.
    '''
    raise NotImplementedError('The kill function is not implemented, make an agent subclass to implement the method')

`load(folder)` `classmethod`

Function to load a trained agent from long term storage. By default, as for the save function, it will load the agent from the folder assuming it is a pickle file.

Parameters:

Name	Type	Description	Default
`folder`	`str`	The folder in which the agent was saved.	required

Returns:

Name	Type	Description
`loaded_agent`	`Agent`	The agent loaded from the folder.

Source code in olfactory_navigation/agent.py

@classmethod
def load(cls,
         folder: str
         ) -> 'Agent':
    '''
    Function to load a trained agent from long term storage.
    By default, as for the save function, it will load the agent from the folder assuming it is a pickle file.

    Parameters
    ----------
    folder : str
        The folder in which the agent was saved.

    Returns
    -------
    loaded_agent : Agent
        The agent loaded from the folder.
    '''
    from olfactory_navigation import agents

    for name, obj in inspect.getmembers(agents):
        if inspect.isclass(obj) and (name in folder) and issubclass(obj, cls) and (obj != cls):
            return obj.load(folder)

    # Default loading with pickle
    with open(folder + '/binary.pkl', 'rb') as f:
        return pickle.load(f)

`modify_environment(new_environment)`

Function to modify the environment of the agent.

Note: By default, a new agent is created with the same threshold and name but with a this new environment! If there are any trained elements to the agent, they are to be modified in this method to be adapted to this new environment.

Parameters:

Name	Type	Description	Default
`new_environment`	`Environment`	The new environment to replace the agent in an equivalent agent.	required

Returns:

Name	Type	Description
`modified_agent`	`Agent`	A new Agent whose environment has been replaced.

Source code in olfactory_navigation/agent.py

def modify_environment(self,
                       new_environment: Environment
                       ) -> 'Agent':
    '''
    Function to modify the environment of the agent.

    Note: By default, a new agent is created with the same threshold and name but with a this new environment!
    If there are any trained elements to the agent, they are to be modified in this method to be adapted to this new environment.

    Parameters
    ----------
    new_environment : Environment
        The new environment to replace the agent in an equivalent agent.

    Returns
    -------
    modified_agent : Agent
        A new Agent whose environment has been replaced.
    '''
    modified_agent = self.__class__(environment=new_environment,
                                    threshold=self.threshold,
                                    name=self.name)
    return modified_agent

`save(folder=None, force=False, save_environment=False)`

Function to save a trained agent to long term storage. By default, the agent is saved in its entirety using pickle.

However, it is strongly advised to overwrite this method to only save save the necessary components of the agents in order to be able to load it and reproduce its behavior. For instance, if the agent is saved after the simulation is run, the state would also be saved within the pickle which is not wanted.

Parameters:

Name	Type	Description	Default
`folder`	`str`	The folder in which the agent's data should be saved.	`None`
`force`	`bool`	If the agent is already saved at the folder provided, the saving should fail. If the already saved agent should be overwritten, this parameter should be toggled to True.	`False`
`save_environment`	`bool`	Whether to save the agent's linked environment alongside the agent itself.	`False`

Source code in olfactory_navigation/agent.py

def save(self,
         folder: str | None = None,
         force: bool = False,
         save_environment: bool = False
         ) -> None:
    '''
    Function to save a trained agent to long term storage.
    By default, the agent is saved in its entirety using pickle.

    However, it is strongly advised to overwrite this method to only save save the necessary components of the agents in order to be able to load it and reproduce its behavior.
    For instance, if the agent is saved after the simulation is run, the state would also be saved within the pickle which is not wanted.

    Parameters
    ----------
    folder : str, optional
        The folder in which the agent's data should be saved.
    force : bool, default=False
        If the agent is already saved at the folder provided, the saving should fail.
        If the already saved agent should be overwritten, this parameter should be toggled to True.
    save_environment : bool, default=False
        Whether to save the agent's linked environment alongside the agent itself.
    '''
    if self.on_gpu:
        cpu_agent = self.to_cpu()
        cpu_agent.save(folder=folder, force=force, save_environment=save_environment)
        return

    # Adding env name to folder path
    if folder is None:
        folder = f'./Agent-{self.name}'
    else:
        folder += f'/Agent-{self.name}'

    # Checking the folder exists or creates it
    if not os.path.exists(folder):
        os.mkdir(folder)
    elif len(os.listdir(folder)) > 0:
        if force:
            shutil.rmtree(folder)
            os.mkdir(folder)
        else:
            raise Exception(f'{folder} is not empty. If you want to overwrite the saved agent, enable "force".')

    # Send self to pickle
    with open(folder + '/binary.pkl', 'wb') as f:
        pickle.dump(self, f)

    # Save environment in folder too if requested
    if save_environment:
        self.environment.save(folder=(folder + f'/Env-{self.environment.name}'))

`to_cpu()`

Function to send the numpy arrays of the agent to the gpu. It returns a new instance of the Agent class with the arrays on the gpu.

Returns:

Name	Type	Description
`cpu_agent`	`Agent`	A new environment instance where the arrays are on the cpu memory.

Source code in olfactory_navigation/agent.py

def to_cpu(self) -> 'Agent':
    '''
    Function to send the numpy arrays of the agent to the gpu.
    It returns a new instance of the Agent class with the arrays on the gpu.

    Returns
    -------
    cpu_agent : Agent
        A new environment instance where the arrays are on the cpu memory.
    '''
    if self.on_gpu:
        assert self._alternate_version is not None, "Something went wrong"
        return self._alternate_version

    return self

`to_gpu()`

Function to send the numpy arrays of the agent to the gpu. It returns a new instance of the Agent class with the arrays on the gpu.

Returns:

Name	Type	Description
`gpu_agent`	`Agent`	A new environment instance where the arrays are on the gpu memory.

Source code in olfactory_navigation/agent.py

def to_gpu(self) -> 'Agent':
    '''
    Function to send the numpy arrays of the agent to the gpu.
    It returns a new instance of the Agent class with the arrays on the gpu.

    Returns
    -------
    gpu_agent : Agent
        A new environment instance where the arrays are on the gpu memory.
    '''
    assert gpu_support, "GPU support is not enabled, Cupy might need to be installed..."

    # Generating a new instance
    cls = self.__class__
    gpu_agent = cls.__new__(cls)

    # Copying arguments to gpu
    for arg, val in self.__dict__.items():
        if isinstance(val, np.ndarray):
            setattr(gpu_agent, arg, cp.array(val))
        elif arg == 'rnd_state':
            setattr(gpu_agent, arg, cp.random.RandomState(self.seed))
        else:
            setattr(gpu_agent, arg, val)

    # Self reference instances
    self._alternate_version = gpu_agent
    gpu_agent._alternate_version = self

    gpu_agent.on_gpu = True
    return gpu_agent

`train()`

Optional function to train the agent in the olfactory environment it is in. This function is optional as some agents have some fixed behavior and therefore dont require training.

Source code in olfactory_navigation/agent.py

def train(self) -> None:
    '''
    Optional function to train the agent in the olfactory environment it is in.
    This function is optional as some agents have some fixed behavior and therefore dont require training.
    '''
    raise NotImplementedError('The train function is not implemented, make an agent subclass to implement the method')

`update_state(observation, source_reached)`

Function to update the internal state(s) of the agent(s) based on the action(s) taken and the observation(s) received. The observations are then compared with the threshold to decide whether something was sensed or not.

Parameters:

Name	Type	Description	Default
`observation`	`ndarray`	A 1D array of odor cues (float values) retrieved from the environment.	required
`source_reached`	`array`	A 1D array of boolean values signifying whether each agent reached or not the source.	required

Returns:

Name	Type	Description
`update_successfull`	`(ndarray, optional)`	If nothing is returned, it means all the agent's state updates have been successfull. Else, a boolean np.ndarray of size n can be returned confirming for each agent whether the update has been successful or not.

Source code in olfactory_navigation/agent.py

def update_state(self,
                 observation: np.ndarray,
                 source_reached: np.ndarray
                 ) -> None | np.ndarray:
    '''
    Function to update the internal state(s) of the agent(s) based on the action(s) taken and the observation(s) received.
    The observations are then compared with the threshold to decide whether something was sensed or not.

    Parameters
    ----------
    observation : np.ndarray
        A 1D array of odor cues (float values) retrieved from the environment.
    source_reached : np.array
        A 1D array of boolean values signifying whether each agent reached or not the source.

    Returns
    -------
    update_successfull : np.ndarray, optional
        If nothing is returned, it means all the agent's state updates have been successfull.
        Else, a boolean np.ndarray of size n can be returned confirming for each agent whether the update has been successful or not.
    '''
    raise NotImplementedError('The update_state function is not implemented, make an agent subclass to implement the method')

agent

Agent

class_name property

choose_action()

initialize_state(n=1)

kill(simulations_to_kill)

load(folder) classmethod

modify_environment(new_environment)

save(folder=None, force=False, save_environment=False)

to_cpu()

to_gpu()

train()

update_state(observation, source_reached)

`Agent`

`class_name` `property`

`choose_action()`

`initialize_state(n=1)`

`kill(simulations_to_kill)`

`load(folder)` `classmethod`

`modify_environment(new_environment)`

`save(folder=None, force=False, save_environment=False)`

`to_cpu()`

`to_gpu()`

`train()`

`update_state(observation, source_reached)`