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#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
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#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
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import os
import paddle
from parl.core.agent_base import AgentBase
from parl.core.paddle.algorithm import Algorithm
from parl.utils import machine_info, get_gpu_count
__all__ = ['Agent']
[文档]class Agent(AgentBase):
"""
| `alias`: ``parl.Agent``
| `alias`: ``parl.core.paddle.agent.Agent``
| Agent is one of the three basic classes of PARL.
| It is responsible for interacting with the environment and collecting
data for training the policy.
| To implement a customized ``Agent``, users can:
.. code-block:: python
import parl
class MyAgent(parl.Agent):
def __init__(self, algorithm, act_dim):
super(MyAgent, self).__init__(algorithm)
self.act_dim = act_dim
Attributes:
alg (parl.algorithm): algorithm of this agent.
place: can automatically specify device when creating a tensor.
Public Functions:
- ``sample``: return a noisy action to perform exploration according to the policy.
- ``predict``: return an action given current observation.
- ``learn``: update the parameters of self.alg using the `learn_program` defined in `build_program()`.
- ``save``: save parameters of the ``agent`` to a given path.
- ``restore``: restore previous saved parameters from a given path.
Todo:
- allow users to get parameters of a specified model by specifying the model's name in ``get_weights()``.
"""
[文档] def __init__(self, algorithm):
"""
Args:
algorithm (parl.Algorithm): an instance of `parl.Algorithm`. This algorithm is then passed to `self.alg`.
"""
assert isinstance(algorithm, Algorithm)
super(Agent, self).__init__(algorithm)
gpu_count = get_gpu_count()
if gpu_count > 0:
self.place = paddle.CUDAPlace(0)
else:
self.place = paddle.CPUPlace()
[文档] def learn(self, *args, **kwargs):
"""The training interface for ``Agent``.
"""
raise NotImplementedError
[文档] def predict(self, *args, **kwargs):
"""Predict an action when given the observation of the environment.
"""
raise NotImplementedError
[文档] def sample(self, *args, **kwargs):
"""Return an action with noise when given the observation of the environment.
In general, this function is used in train process as noise is added to the action to preform exploration.
"""
raise NotImplementedError
[文档] def save(self, save_path, model=None):
"""Save parameters.
Args:
save_path(str): where to save the parameters.
model(parl.Model): model that describes the neural network structure. If None, will use self.alg.model.
Raises:
ValueError: if program is None and self.learn_program does not exist.
Example:
.. code-block:: python
agent = AtariAgent()
agent.save('./model_dir')
"""
if model is None:
model = self.alg.model
paddle.save(model.state_dict(), save_path)
[文档] def restore(self, save_path, model=None):
"""Restore previously saved parameters.
This method requires a program that describes the network structure.
The save_path argument is typically a value previously passed to ``save_params()``.
Args:
save_path(str): path where parameters were previously saved.
model(parl.Model): model that describes the neural network structure. If None, will use self.alg.model.
Raises:
ValueError: if program is None and self.learn_program does not exist.
Example:
.. code-block:: python
agent = AtariAgent()
agent.save('./model_dir')
agent.restore('./model_dir')
"""
if model is None:
model = self.alg.model
param_dict = paddle.load(save_path)
model.set_state_dict(param_dict)