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- # Copyright (c) 2018 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 numpy as np
- import re
- import parl
- from parl import layers
- from paddle import fluid
- from paddle.fluid.executor import _fetch_var
- from parl.utils import logger
-
-
- class OpenSimAgent(parl.Agent):
- def __init__(self, algorithm, obs_dim, act_dim, ensemble_num):
- self.obs_dim = obs_dim
- self.act_dim = act_dim
- self.ensemble_num = ensemble_num
- super(OpenSimAgent, self).__init__(algorithm)
-
- # Use ParallelExecutor to make program running faster
- use_cuda = True if parl.GPU_ID >= 0 else False
- self.learn_pe = []
- self.pred_pe = []
-
- exec_strategy = fluid.ExecutionStrategy()
- exec_strategy.use_experimental_executor = True
- exec_strategy.num_threads = 4
- build_strategy = fluid.BuildStrategy()
- build_strategy.remove_unnecessary_lock = True
-
- for i in range(self.ensemble_num):
- with fluid.scope_guard(fluid.global_scope().new_scope()):
- pe = fluid.ParallelExecutor(
- use_cuda=use_cuda,
- main_program=self.learn_programs[i],
- exec_strategy=exec_strategy,
- build_strategy=build_strategy)
- self.learn_pe.append(pe)
-
- with fluid.scope_guard(fluid.global_scope().new_scope()):
- pe = fluid.ParallelExecutor(
- use_cuda=use_cuda,
- main_program=self.predict_programs[i],
- exec_strategy=exec_strategy,
- build_strategy=build_strategy)
- self.pred_pe.append(pe)
-
- # Attention: In the beginning, sync target model totally.
- self.alg.sync_target(
- model_id=i,
- decay=0,
- share_vars_parallel_executor=self.learn_pe[i])
- # Do cache, will create ParallelExecutor of sync params in advance
- # If not, there are some issues when ensemble_num > 1
- self.alg.sync_target(
- model_id=i, share_vars_parallel_executor=self.learn_pe[i])
-
- with fluid.scope_guard(fluid.global_scope().new_scope()):
- self.ensemble_predict_pe = fluid.ParallelExecutor(
- use_cuda=use_cuda,
- main_program=self.ensemble_predict_program,
- exec_strategy=exec_strategy,
- build_strategy=build_strategy)
-
- def build_program(self):
- self.predict_programs = []
- self.predict_outputs = []
- self.learn_programs = []
- self.learn_programs_output = []
- for i in range(self.ensemble_num):
- predict_program = fluid.Program()
- with fluid.program_guard(predict_program):
- obs = layers.data(
- name='obs', shape=[self.obs_dim], dtype='float32')
- act = self.alg.predict(obs, model_id=i)
- self.predict_programs.append(predict_program)
- self.predict_outputs.append([act.name])
-
- learn_program = fluid.Program()
- with fluid.program_guard(learn_program):
- obs = layers.data(
- name='obs', shape=[self.obs_dim], dtype='float32')
- act = layers.data(
- name='act', shape=[self.act_dim], dtype='float32')
- reward = layers.data(name='reward', shape=[], dtype='float32')
- next_obs = layers.data(
- name='next_obs', shape=[self.obs_dim], dtype='float32')
- terminal = layers.data(name='terminal', shape=[], dtype='bool')
- actor_lr = layers.data(
- name='actor_lr',
- shape=[1],
- dtype='float32',
- append_batch_size=False)
- critic_lr = layers.data(
- name='critic_lr',
- shape=[1],
- dtype='float32',
- append_batch_size=False)
- actor_loss, critic_loss = self.alg.learn(
- obs,
- act,
- reward,
- next_obs,
- terminal,
- actor_lr,
- critic_lr,
- model_id=i)
- self.learn_programs.append(learn_program)
- self.learn_programs_output.append([critic_loss.name])
-
- self.ensemble_predict_program = fluid.Program()
- with fluid.program_guard(self.ensemble_predict_program):
- obs = layers.data(
- name='obs', shape=[self.obs_dim], dtype='float32')
- act = self.alg.ensemble_predict(obs)
- self.ensemble_predict_output = [act.name]
-
- def predict(self, obs, model_id):
- feed = {'obs': obs}
- feed = [feed]
- act = self.pred_pe[model_id].run(
- feed=feed, fetch_list=self.predict_outputs[model_id])[0]
- return act
-
- def ensemble_predict(self, obs):
- feed = {'obs': obs}
- feed = [feed]
- act = self.ensemble_predict_pe.run(
- feed=feed, fetch_list=self.ensemble_predict_output)[0]
- return act
-
- def learn(self, obs, act, reward, next_obs, terminal, actor_lr, critic_lr,
- model_id):
- feed = {
- 'obs': obs,
- 'act': act,
- 'reward': reward,
- 'next_obs': next_obs,
- 'terminal': terminal,
- 'actor_lr': actor_lr,
- 'critic_lr': critic_lr
- }
-
- feed = [feed]
- critic_loss = self.learn_pe[model_id].run(
- feed=feed, fetch_list=self.learn_programs_output[model_id])[0]
- self.alg.sync_target(
- model_id=model_id,
- share_vars_parallel_executor=self.learn_pe[model_id])
- return critic_loss
-
- def save_params(self, dirname):
- for i in range(self.ensemble_num):
- fluid.io.save_params(
- executor=self.fluid_executor,
- dirname=dirname,
- main_program=self.learn_programs[i])
-
- def load_params(self, dirname, from_one_head):
- if from_one_head:
- logger.info('[From one head, extend to multi head:]')
- # load model 0
- fluid.io.load_params(
- executor=self.fluid_executor,
- dirname=dirname,
- main_program=self.learn_programs[0])
-
- # sync identity params of model/target_model 0 to other models/target_models
- for i in range(1, self.ensemble_num):
- params = list(
- filter(
- lambda x: 'identity' in x.name and '@GRAD' not in x.name,
- self.learn_programs[i].list_vars()))
- for param in params:
- param_var = _fetch_var(param.name, return_numpy=False)
-
- model0_name = re.sub(r"identity_\d+", "identity_0",
- param.name)
- model0_value = _fetch_var(model0_name, return_numpy=True)
- logger.info('{} -> {}'.format(model0_name, param.name))
- param_var.set(model0_value, self.place)
-
- # sync share params of target_model 0 to other target models
- # After deepcopy, shapre params between target models is different
- for i in range(1, self.ensemble_num):
- params = list(
- filter(
- lambda x: 'shared' in x.name and 'PARL_target' in x.name and '@GRAD' not in x.name,
- self.learn_programs[i].list_vars()))
- for param in params:
- param_var = _fetch_var(param.name, return_numpy=False)
-
- model0_name = re.sub(r"_\d+$", "_0", param.name)
- model0_value = _fetch_var(model0_name, return_numpy=True)
- logger.info('{} -> {}'.format(model0_name, param.name))
- param_var.set(model0_value, self.place)
-
- else:
- for i in range(self.ensemble_num):
- fluid.io.load_params(
- executor=self.fluid_executor,
- dirname=dirname,
- main_program=self.learn_programs[i])
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