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RLFEM#

RLFEM.RLFEM #

RLFEM #

Environment allowing to interact with the FreeCad simulation.

TODO: more description

__init__(self, path='') special #

Parameters:

Name Type Description Default
path

path to the .ply file.

 ''
Source code in RLFEM/RLFEM.py 
def __init__(self, path=''):
    """
    :param path: path to the .ply file.
    """
    self.path = path

    self.interface = FreeCADWrapper(self.path)

    self.state = self.reset()

    super().__init__()

close(self) #

To be called before exiting, to free resources.

Source code in RLFEM/RLFEM.py 
def close(self):
    "To be called before exiting, to free resources."
    pass

render(self, mode='human') #

Displays the current state of the environment.

Can be text or video frames.

Source code in RLFEM/RLFEM.py 
def render(self, mode='human'):
    """Displays the current state of the environment. 

    Can be text or video frames.
    """

    print(self.state)

reset(self) #

Resets the environment and starts from an initial state.

Returns:

Type Description

the initial state.
Source code in RLFEM/RLFEM.py 
def reset(self):
    """Resets the environment and starts from an initial state.

    :returns: the initial state.
    """

    # Initial state
    # self.state = self.interface.reset()

    return self.state

step(self, action) #

Takes an action in the environment and returns a new state, a reward, a boolean (True for terminal states) and a dictionary with additional info (optional).

Parameters:

Name Type Description Default
action

the action to be taken.

 required

Returns:

Type Description

(next_state, reward, done, info)
Source code in RLFEM/RLFEM.py 
def step(self, action):
    """Takes an action in the environment and returns a new state, a reward, a boolean (True for terminal states) 
    and a dictionary with additional info (optional).

    :param action: the action to be taken.

    :returns: (next_state, reward, done, info)
    """

    self.state = self.interface.apply(action) # Random transition to another state
    self.reward = np.random.uniform(0, 1, 1)[0] # Random reward
    self.done = False # Continuing task
    self.info = {} # No info

    return self.state, self.reward, self.done, self.info