Model (*Forward Part*) ======================= Methods ---------- 1. policy(self, *args) Define the structure of networks here. Algorithm will call this method to predict probabilities of actions. It is optional. 2. value(self, *args) Return: values: a dict of estimated values for the current observations and states. For example, "q_value" and "v_value". 3. sync_params_to(self, target_net, gpu_id, decay=0.0, share_vars_parallel_executor=None) This method deepcopied the parameters from the current network to the target network, which two have the same structure. An example ------------ .. code-block:: python :linenos: class MLPModel(Model): def __init__(self): self.fc = layers.fc(size=64) def policy(self, obs): out = self.fc(obs) return out model = MLPModel() target_model = deepcopy(model) # automatically create new unique parameters names for target_model.fc # build program x = layers.data(name='x', shape=[100], dtype="float32") y1 = model.policy(x) y2 = target_model.policy(x)
