用parl框架搭建DDPG

如何插入一段漂亮的代码片

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// An highlighted blockimport os
import numpy as np
import parl
from parl import layers
from paddle import fluid
from parl.utils import logger
from parl.utils import action_mapping # 将神经网络输出映射到对应的 实际动作取值范围 内
from parl.utils import ReplayMemory # 经验回放
from rlschool import make_env  # 使用 RLSchool 创建飞行器环境ACTOR_LR = 0.0002   # Actor网络更新的 learning rate
CRITIC_LR = 0.001   # Critic网络更新的 learning rate
GAMMA = 0.95        # reward 的衰减因子，一般取 0.9 到 0.999 不等
TAU = 0.001         # target_model 跟 model 同步参数 的 软更新参数
MEMORY_SIZE = 1e6   # replay memory的大小，越大越占用内存
MEMORY_WARMUP_SIZE = 1e4      # replay_memory 里需要预存一些经验数据，再从里面sample一个batch的经验让agent去learn
REWARD_SCALE = 0.01       # reward 的缩放因子
BATCH_SIZE = 256          # 每次给agent learn的数据数量，从replay memory随机里sample一批数据出来
TRAIN_TOTAL_STEPS = 1e6*2   # 总训练步数
TEST_EVERY_STEPS = 1e4    # 每个N步评估一下算法效果，每次评估5个episode求平均reward#构造网络结构  演员家网络
class ActorModel(parl.Model):def __init__(self, act_dim):hid_size = 100self.fc1 = layers.fc(size=hid_size, act='relu')self.fc2 = layers.fc(size=act_dim, act='tanh')def policy(self, obs):hid = self.fc1(obs)logits = self.fc2(hid)#action_mapping(action, env.action_space.low[0],env.action_space.high[0])return logits
#评论家网络
class CriticModel(parl.Model):def __init__(self):hid_size = 100self.fc1 = layers.fc(size=hid_size, act='relu')self.fc2 = layers.fc(size=1, act=None)def value(self, obs, act):concat = layers.concat([obs, act], axis=1)hid = self.fc1(concat)Q = self.fc2(hid)Q = layers.squeeze(Q, axes=[1])return Q
#统一嵌套入一个网络中 
class QuadrotorModel(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()#可以直接调用parl框架中的算法步骤，也可以自己写
from parl.algorithms import DDPG#构造智能体结构，从外面要数据，训练以及决策
class QuadrotorAgent(parl.Agent):def __init__(self, algorithm, obs_dim, act_dim=4):assert isinstance(obs_dim, int)assert isinstance(act_dim, int)self.obs_dim = obs_dimself.act_dim = act_dimsuper(QuadrotorAgent, self).__init__(algorithm)# 注意，在最开始的时候，先完全同步target_model和model的参数self.alg.sync_target(decay=0)def build_program(self):self.pred_program = fluid.Program()self.learn_program = fluid.Program()with fluid.program_guard(self.pred_program):obs = layers.data(name='obs', shape=[self.obs_dim], dtype='float32')self.pred_act = self.alg.predict(obs)with fluid.program_guard(self.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')_, self.critic_cost = self.alg.learn(obs, act, reward, next_obs,terminal)def predict(self, obs):obs = np.expand_dims(obs, axis=0)act = self.fluid_executor.run(self.pred_program, feed={'obs': obs},fetch_list=[self.pred_act])[0]return actdef learn(self, obs, act, reward, next_obs, terminal):feed = {'obs': obs,'act': act,'reward': reward,'next_obs': next_obs,'terminal': terminal}critic_cost = self.fluid_executor.run(self.learn_program, feed=feed, fetch_list=[self.critic_cost])[0]self.alg.sync_target()return critic_cost