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#
# 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
from paddle.fluid.param_attr import ParamAttr
class AtariModel(parl.Model):
def __init__(self, act_dim):
self.conv1 = layers.conv2d(
num_filters=16, filter_size=4, stride=2, padding=1, act='relu')
self.conv2 = layers.conv2d(
num_filters=32, filter_size=4, stride=2, padding=2, act='relu')
self.conv3 = layers.conv2d(
num_filters=256, filter_size=11, stride=1, padding=0, act='relu')
self.policy_conv = layers.conv2d(
num_filters=act_dim,
filter_size=1,
stride=1,
padding=0,
act=None,
param_attr=ParamAttr(initializer=fluid.initializer.Normal()))
# 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 numpy as np
import parl
from parl import layers
from paddle import fluid
from paddle.fluid.param_attr import ParamAttr
class MujocoModel(parl.Model):
def __init__(self, obs_dim, act_dim, init_logvar=-1.0):
self.policy_model = PolicyModel(obs_dim, act_dim, init_logvar)
self.value_model = ValueModel(obs_dim, act_dim)
self.policy_lr = self.policy_model.lr
self.value_lr = self.value_model.lr
def policy(self, obs):
return self.policy_model.policy(obs)
def policy_sample(self, obs):
return self.policy_model.sample(obs)
def value(self, obs):
return self.value_model.value(obs)
# 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 os
import paddle.fluid as fluid
from parl import layers
import numpy as np
from parl import Model
from parl import Agent
from parl.utils import get_gpu_count
class RLDispatcherModel(Model):
def __init__(self, act_dim):
self._act_dim = act_dim
self._fc_1 = layers.fc(size=512, act='relu')
self._fc_2 = layers.fc(size=256, act='relu')
self._fc_3 = layers.fc(size=128, act='tanh')
self._output = layers.fc(size=act_dim)
def value(self, obs):
self._h_1 = self._fc_1(obs)
self._h_2 = self._fc_2(self._h_1)
self._h_3 = self._fc_3(self._h_2)
self._pred = self._output(self._h_3)
return self._pred
#
# 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
from parl import layers
from paddle import fluid
from paddle.fluid.param_attr import ParamAttr
class OpenSimModel(parl.Model):
def __init__(self, obs_dim, vel_obs_dim, act_dim, model_id=0, shared=True):
self.actor_model = ActorModel(obs_dim, vel_obs_dim, act_dim, model_id,
shared)
self.critic_model = CriticModel(obs_dim, vel_obs_dim, act_dim,
model_id, shared)
def policy(self, obs):
return self.actor_model.policy(obs)
def value(self, obs, action):
return self.critic_model.value(obs, action)
def get_actor_params(self):
return self.actor_model.parameters()
hid1_size = 400
hid2_size = 300
self.fc1 = layers.fc(size=hid1_size, act='relu')
self.fc2 = layers.fc(size=hid2_size, act='relu')
self.fc3 = layers.fc(size=act_dim, act='tanh')
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 = 400
hid2_size = 300
self.fc1 = layers.fc(size=hid1_size, act='relu')
self.fc2 = layers.fc(size=hid2_size, act='relu')
self.fc3 = layers.fc(size=1, act=None)
def value(self, obs, act):
hid1 = self.fc1(obs)
concat = layers.concat([hid1, act], axis=1)
hid2 = self.fc2(concat)
Q = self.fc3(hid2)
Q = layers.squeeze(Q, axes=[1])
return Q
# 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 AtariModel(parl.Model):
def __init__(self, act_dim, algo='DQN'):
self.act_dim = act_dim
self.conv1 = layers.conv2d(
num_filters=32, filter_size=5, stride=1, padding=2, act='relu')
self.conv2 = layers.conv2d(
num_filters=32, filter_size=5, stride=1, padding=2, act='relu')
self.conv3 = layers.conv2d(
num_filters=64, filter_size=4, stride=1, padding=1, act='relu')
self.conv4 = layers.conv2d(
num_filters=64, filter_size=3, stride=1, padding=1, act='relu')
self.algo = algo
if algo == 'Dueling':
self.fc1_adv = layers.fc(size=512, act='relu')
self.fc2_adv = layers.fc(size=act_dim)
# 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.fluid as fluid
from parl import layers
class AtariModel(parl.Model):
def __init__(self, act_dim):
self.conv1 = layers.conv2d(
num_filters=32, filter_size=8, stride=4, padding=1, act='relu')
self.conv2 = layers.conv2d(
num_filters=64, filter_size=4, stride=2, padding=2, act='relu')
self.conv3 = layers.conv2d(
num_filters=64, filter_size=3, stride=1, padding=0, act='relu')
self.fc = layers.fc(size=512, act='relu')
self.policy_fc = layers.fc(size=act_dim)
self.value_fc = layers.fc(size=1)
def policy(self, obs):
"""
self.actor_model = ActorModel(obs_dim, vel_obs_dim, act_dim, model_id,
shared)
self.critic_model = CriticModel(obs_dim, vel_obs_dim, act_dim,
model_id, shared)
def policy(self, obs):
return self.actor_model.policy(obs)
def value(self, obs, action):
return self.critic_model.value(obs, action)
def get_actor_params(self):
return self.actor_model.parameters()
class ActorModel(parl.Model):
def __init__(self, obs_dim, vel_obs_dim, act_dim, model_id, shared):
hid0_size = 800
hid1_size = 400
hid2_size = 200
vel_hid0_size = 200
vel_hid1_size = 400
self.obs_dim = obs_dim
self.vel_obs_dim = vel_obs_dim
# bottom layers
if shared:
scope_name = 'policy_shared'
else:
scope_name = 'policy_identity_{}'.format(model_id)
class MujocoModel(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()
class ActorModel(parl.Model):
def __init__(self, act_dim):
hid1_size = 400
hid2_size = 300
self.fc1 = layers.fc(size=hid1_size, act='relu')
self.fc2 = layers.fc(size=hid2_size, act='relu')
self.fc3 = layers.fc(size=act_dim, act='tanh')
def policy(self, obs):
hid1 = self.fc1(obs)
hid2 = self.fc2(hid1)
means = self.fc3(hid2)
means = means
return means
self.policy_model = PolicyModel(obs_dim, act_dim, init_logvar)
self.value_model = ValueModel(obs_dim, act_dim)
self.policy_lr = self.policy_model.lr
self.value_lr = self.value_model.lr
def policy(self, obs):
return self.policy_model.policy(obs)
def policy_sample(self, obs):
return self.policy_model.sample(obs)
def value(self, obs):
return self.value_model.value(obs)
class PolicyModel(parl.Model):
def __init__(self, obs_dim, act_dim, init_logvar):
self.obs_dim = obs_dim
self.act_dim = act_dim
hid1_size = obs_dim * 10
hid3_size = act_dim * 10
hid2_size = int(np.sqrt(hid1_size * hid3_size))
self.lr = 9e-4 / np.sqrt(hid2_size)
self.fc1 = layers.fc(size=hid1_size, act='tanh')
self.fc2 = layers.fc(size=hid2_size, act='tanh')
self.fc3 = layers.fc(size=hid3_size, act='tanh')
self.fc4 = layers.fc(size=act_dim, act='tanh')
self.logvars = layers.create_parameter(
shape=[act_dim],