class_docstr: | `alias`: ``parl.Algorithm``
  | `alias`: ``parl.core.torch.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:
      - ``predict``: return an estimate q value given current observation.
      - ``learn``: define the loss function and create an optimizer to
      minimize the loss.
docstr: Refine the predicting process, e.g,. use the policy model to
  predict actions.
code: def predict(self, *args, **kwargs):
      """Refine the predicting process, e.g,. use the policy model to
      predict actions.
      """
      raise NotImplementedError
  
file_path: PARL/parl/core/torch/algorithm.py
imports: import warnings
  from parl.core.algorithm_base import AlgorithmBase
  from parl.core.torch.model import Model
label_desc: Use image node_id 5 for calling the Algorithm obj's underlying member method code with example usage: obj.predict() without return types