Question

class Algorithm(model=None)[源代码]¶

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:

import parl

model = Model()
dqn = parl.algorithms.DQN(model, lr=1e-3)

变量:

• model (parl.Model) – a neural network that represents a policy

• function. (or a Q-value) –

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.

__init__(model=None)[源代码]¶

参数:

model (parl.Model) – a neural network that represents a policy or a Q-value function.

get_weights()[源代码]¶

Get weights of self.model.

返回:

a Python dict containing the parameters of self.model.

返回类型:

weights (dict)

learn(*args, **kwargs)[源代码]¶

Define the loss function and create an optimizer to minize the loss.

predict(*args, **kwargs)[源代码]¶

Refine the predicting process, e.g,. use the policy model to predict actions.

sample(*args, **kwargs)[源代码]¶

Define the sampling process. This function returns an action with noise to perform exploration.

set_weights(params)[源代码]¶

Set weights from get_weights to the model.

参数:

weights (dict) – a Python dict containing the parameters of self.model.