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- # Copyright (c) 2021 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 numpy as np
-
-
- class MujocoAgent(parl.Agent):
- def __init__(self, algorithm, act_dim, expl_noise=0.1):
- assert isinstance(act_dim, int)
- super(MujocoAgent, self).__init__(algorithm)
-
- self.act_dim = act_dim
- self.expl_noise = expl_noise
-
- self.alg.sync_target(decay=0)
-
- def sample(self, obs):
- action_numpy = self.predict(obs)
- action_noise = np.random.normal(0, self.expl_noise, size=self.act_dim)
- action = (action_numpy + action_noise).clip(-1, 1)
- return action
-
- def predict(self, obs):
- obs = paddle.to_tensor(obs.reshape(1, -1), dtype='float32')
- action = self.alg.predict(obs)
- action_numpy = action.cpu().numpy()[0]
- return action_numpy
-
- def learn(self, obs, action, reward, next_obs, terminal):
- terminal = np.expand_dims(terminal, -1)
- reward = np.expand_dims(reward, -1)
-
- obs = paddle.to_tensor(obs, dtype='float32')
- action = paddle.to_tensor(action, dtype='float32')
- reward = paddle.to_tensor(reward, dtype='float32')
- next_obs = paddle.to_tensor(next_obs, dtype='float32')
- terminal = paddle.to_tensor(terminal, dtype='float32')
- critic_loss, actor_loss = self.alg.learn(obs, action, reward, next_obs,
- terminal)
- return critic_loss, actor_loss
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