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simple_model.py
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# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import paddle.fluid as fluid
import parl
from parl import layers
class MAModel(parl.Model):
def __init__(self, act_dim):
self.actor_model = ActorModel(act_dim)
self.critic_model = CriticModel()
def policy(self, obs):
return self.actor_model.policy(obs)
def value(self, obs, act):
return self.critic_model.value(obs, act)
def get_actor_params(self):
return self.actor_model.parameters()
def get_critic_params(self):
return self.critic_model.parameters()
class ActorModel(parl.Model):
def __init__(self, act_dim):
hid1_size = 64
hid2_size = 64
self.fc1 = layers.fc(
size=hid1_size,
act='relu',
param_attr=fluid.initializer.Normal(loc=0.0, scale=0.1))
self.fc2 = layers.fc(
size=hid2_size,
act='relu',
param_attr=fluid.initializer.Normal(loc=0.0, scale=0.1))
self.fc3 = layers.fc(
size=act_dim,
act=None,
param_attr=fluid.initializer.Normal(loc=0.0, scale=0.1))
def policy(self, obs):
hid1 = self.fc1(obs)
hid2 = self.fc2(hid1)
means = self.fc3(hid2)
means = means
return means
class CriticModel(parl.Model):
def __init__(self):
hid1_size = 64
hid2_size = 64
self.fc1 = layers.fc(
size=hid1_size,
act='relu',
param_attr=fluid.initializer.Normal(loc=0.0, scale=0.1))
self.fc2 = layers.fc(
size=hid2_size,
act='relu',
param_attr=fluid.initializer.Normal(loc=0.0, scale=0.1))
self.fc3 = layers.fc(
size=1,
act=None,
param_attr=fluid.initializer.Normal(loc=0.0, scale=0.1))
def value(self, obs_n, act_n):
inputs = layers.concat(obs_n + act_n, axis=1)
hid1 = self.fc1(inputs)
hid2 = self.fc2(hid1)
Q = self.fc3(hid2)
Q = layers.squeeze(Q, axes=[1])
return Q