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# Licensed under the Apache License, Version 2.0 (the "License");
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# http://www.apache.org/licenses/LICENSE-2.0
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from parl.core.algorithm_base import AlgorithmBase
from parl.core.paddle.model import Model
__all__ = ['Algorithm']
[docs]class Algorithm(AlgorithmBase):
"""
| `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.
"""
[docs] def __init__(self, model=None):
"""
Args:
model(``parl.Model``): a neural network that represents a policy or a Q-value function.
"""
assert isinstance(model, Model)
self.model = model
[docs] def learn(self, *args, **kwargs):
""" Define the loss function and create an optimizer to minize the loss.
"""
raise NotImplementedError
[docs] def predict(self, *args, **kwargs):
""" Refine the predicting process, e.g,. use the policy model to predict actions.
"""
raise NotImplementedError
[docs] def sample(self, *args, **kwargs):
""" Define the sampling process. This function returns an action with noise to perform exploration.
"""
raise NotImplementedError
[docs] def get_weights(self):
""" Get weights of self.model.
Returns:
weights (dict): a Python dict containing the parameters of self.model.
"""
return self.model.get_weights()
[docs] def set_weights(self, params):
""" Set weights from ``get_weights`` to the model.
Args:
weights (dict): a Python dict containing the parameters of self.model.
"""
self.model.set_weights(params)