class_docstr: | `alias`: ``parl.Algorithm``
  | `alias`: ``parl.core.fluid.algorithm.Algorithm``
  
  | ``Algorithm`` defines the way how to update the parameters of 
  the ``Model``. This is where we define loss functions and the 
  optimizer of the neural network. An ``Algorithm`` has at least 
  a model.
  
  | PARL has implemented various algorithms(DQN/DDPG/PPO/A3C/IMPALA) that 
  can be reused quickly, which can be accessed with ``parl.algorithms``.
  
  Example:
  
  .. code-block:: python
  
      import parl
  
      model = Model()
      dqn = parl.algorithms.DQN(model, lr=1e-3)
  
  Attributes:
      model(``parl.Model``): a neural network that represents a policy 
      or a Q-value function.
  
  Pulic Functions:
      - ``get_weights``: return a Python dictionary containing parameters 
      of the current model.
      - ``set_weights``: copy parameters from ``get_weights()`` to the model.
      - ``sample``: return a noisy action to perform exploration according 
      to the policy.
      - ``predict``: return an action given current observation.
      - ``learn``: define the loss function and create an optimizer to 
      minized the loss.
docstr: Define the loss function and create an optimizer to minize the loss.
          
code: def learn(self, *args, **kwargs):
      """Define the loss function and create an optimizer to minize the loss."""
      raise NotImplementedError
  
file_path: PARL/parl/core/paddle/algorithm.py
imports: from parl.core.algorithm_base import AlgorithmBase
  from parl.core.paddle.model import Model
label_desc: Use image node_id 2 for calling the Algorithm obj's underlying member method code with example usage: obj.learn() without return types