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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 paddle
- import parl
- import paddle.nn as nn
- import paddle.nn.functional as F
-
-
- class AtariModel(parl.Model):
- def __init__(self, act_dim):
- super(AtariModel, self).__init__()
-
- self.conv1 = nn.Conv2D(
- in_channels=4, out_channels=16, kernel_size=4, stride=2, padding=1)
- self.conv2 = nn.Conv2D(
- in_channels=16,
- out_channels=32,
- kernel_size=4,
- stride=2,
- padding=2)
- self.conv3 = nn.Conv2D(
- in_channels=32,
- out_channels=256,
- kernel_size=11,
- stride=1,
- padding=0)
-
- self.flatten = nn.Flatten()
-
- # Need to calc the size of the in_features according to the input image.
- # The default size of the input image is 42 * 42
- self.policy_fc = nn.Linear(
- in_features=256,
- out_features=act_dim,
- weight_attr=paddle.ParamAttr(
- initializer=paddle.nn.initializer.Normal()),
- bias_attr=paddle.ParamAttr(
- initializer=paddle.nn.initializer.Normal()))
- self.value_fc = nn.Linear(
- in_features=256,
- out_features=1,
- weight_attr=paddle.ParamAttr(
- initializer=paddle.nn.initializer.Normal()),
- bias_attr=paddle.ParamAttr(
- initializer=paddle.nn.initializer.Normal()))
-
- def policy(self, obs):
- """
- Args:
- obs: A float32 tensor of shape [B, C, H, W]
- Returns:
- policy_logits: B * ACT_DIM
- """
- obs = obs / 255.0
- conv1 = F.relu(self.conv1(obs))
- conv2 = F.relu(self.conv2(conv1))
- conv3 = F.relu(self.conv3(conv2))
- flatten = self.flatten(conv3)
- policy_logits = self.policy_fc(flatten)
-
- return policy_logits
-
- def value(self, obs):
- """
- Args:
- obs: A float32 tensor of shape [B, C, H, W]
-
- Returns:
- values: B
- """
- obs = obs / 255.0
- conv1 = F.relu(self.conv1(obs))
- conv2 = F.relu(self.conv2(conv1))
- conv3 = F.relu(self.conv3(conv2))
- flatten = self.flatten(conv3)
- values = self.value_fc(flatten)
- values = paddle.squeeze(values, axis=1)
- return values
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