# Copyright (c) 2020 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
from paddle.distribution import Categorical
from parl.utils.utils import check_model_method
__all__ = ['PolicyGradient']
[docs]class PolicyGradient(parl.Algorithm):
[docs] def __init__(self, model, lr):
"""Policy gradient algorithm
Args:
model (parl.Model): model defining forward network of policy.
lr (float): learning rate.
"""
# checks
check_model_method(model, 'forward', self.__class__.__name__)
assert isinstance(lr, float)
self.model = model
self.optimizer = paddle.optimizer.Adam(
learning_rate=lr, parameters=self.model.parameters())
[docs] def predict(self, obs):
"""Predict the probability of actions
Args:
obs (paddle tensor): shape of (obs_dim,)
Returns:
prob (paddle tensor): shape of (action_dim,)
"""
prob = self.model(obs)
return prob
[docs] def learn(self, obs, action, reward):
"""Update model with policy gradient algorithm
Args:
obs (paddle tensor): shape of (batch_size, obs_dim)
action (paddle tensor): shape of (batch_size, 1)
reward (paddle tensor): shape of (batch_size, 1)
Returns:
loss (paddle tensor): shape of (1)
"""
prob = self.model(obs)
log_prob = Categorical(prob).log_prob(action)
loss = paddle.mean(-1 * log_prob * reward)
self.optimizer.clear_grad()
loss.backward()
self.optimizer.step()
return loss