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- # Copyright (c) 2022 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 parl
- import paddle
- import paddle.nn as nn
- import paddle.nn.functional as F
-
-
- class MAModel(parl.Model):
- def __init__(self,
- obs_dim,
- act_dim,
- critic_in_dim,
- continuous_actions=False):
- super(MAModel, self).__init__()
- self.actor_model = ActorModel(obs_dim, act_dim, continuous_actions)
- self.critic_model = CriticModel(critic_in_dim)
-
- def policy(self, obs):
- return self.actor_model(obs)
-
- def value(self, obs, act):
- return self.critic_model(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, obs_dim, act_dim, continuous_actions=False):
- super(ActorModel, self).__init__()
- self.continuous_actions = continuous_actions
- hid1_size = 64
- hid2_size = 64
- self.fc1 = nn.Linear(
- obs_dim,
- hid1_size,
- weight_attr=paddle.ParamAttr(
- initializer=paddle.nn.initializer.XavierUniform()))
- self.fc2 = nn.Linear(
- hid1_size,
- hid2_size,
- weight_attr=paddle.ParamAttr(
- initializer=paddle.nn.initializer.XavierUniform()))
- self.fc3 = nn.Linear(
- hid2_size,
- act_dim,
- weight_attr=paddle.ParamAttr(
- initializer=paddle.nn.initializer.XavierUniform()))
- if self.continuous_actions:
- std_hid_size = 64
- self.std_fc = nn.Linear(
- std_hid_size,
- act_dim,
- weight_attr=paddle.ParamAttr(
- initializer=paddle.nn.initializer.XavierUniform()))
-
- def forward(self, obs):
- hid1 = F.relu(self.fc1(obs))
- hid2 = F.relu(self.fc2(hid1))
- means = self.fc3(hid2)
- if self.continuous_actions:
- act_std = self.std_fc(hid2)
- return (means, act_std)
- return means
-
-
- class CriticModel(parl.Model):
- def __init__(self, critic_in_dim):
- super(CriticModel, self).__init__()
- hid1_size = 64
- hid2_size = 64
- out_dim = 1
- self.fc1 = nn.Linear(
- critic_in_dim,
- hid1_size,
- weight_attr=paddle.ParamAttr(
- initializer=paddle.nn.initializer.XavierUniform()))
- self.fc2 = nn.Linear(
- hid1_size,
- hid2_size,
- weight_attr=paddle.ParamAttr(
- initializer=paddle.nn.initializer.XavierUniform()))
- self.fc3 = nn.Linear(
- hid2_size,
- out_dim,
- weight_attr=paddle.ParamAttr(
- initializer=paddle.nn.initializer.XavierUniform()))
-
- def forward(self, obs_n, act_n):
- inputs = paddle.concat(obs_n + act_n, axis=1)
- hid1 = F.relu(self.fc1(inputs))
- hid2 = F.relu(self.fc2(hid1))
- Q = self.fc3(hid2)
- Q = paddle.squeeze(Q, axis=1)
- return Q
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