#!/usr/bin/python
#?-*-?coding:?utf-8?-*-

import?time
import?numpy?as?np
import?gym
from?gym.spaces?import?Discrete?Box

#?================================================================
#?Policies
#?================================================================

class?DeterministicDiscreteActionLinearPolicy（object）:

????def?__init__（self?theta?ob_space?ac_space）:
????????“““
????????dim_ob:?dimension?of?observations
????????n_actions:?number?of?actions
????????theta:?flat?vector?of?parameters
????????“““
????????dim_ob?=?ob_space.shape[0]
????????n_actions?=?ac_space.n
????????assert?len（theta）?==?（dim_ob?+?1）?*?n_actions
????????self.W?=?theta[0?:?dim_ob?*?n_actions].reshape（dim_ob?n_actions）
????????self.b?=?theta[dim_ob?*?n_actions?:?None].reshape（1?n_actions）

????def?act（self?ob）:
????????“““
????????“““
????????y?=?ob.dot（self.W）?+?self.b
????????a?=?y.argmax（）
????????return?a

class?DeterministicContinuousActionLinearPolicy（object）:

????def?__init__（self?theta?ob_space?ac_space）:
????????“““
????????dim_ob:?dimension?of?observations
????????dim_ac:?dimension?of?action?vector
????????theta:?flat?vector?of?parameters
????????“““
????????self.ac_space?=?ac_space
????????dim_ob?=?ob_space.shape[0]
????????dim_ac?=?ac_space.shape[0]
????????assert?len（theta）?==?（dim_ob?+?1）?*?dim_ac
????????self.W?=?theta[0?:?dim_ob?*?dim_ac].reshape（dim_ob?dim_ac）
????????self.b?=?theta[dim_ob?*?dim_ac?:?None]

????def?act（self?ob）:
????????a?=?np.clip（ob.dot（self.W）?+?self.b?self.ac_space.low?self.ac_space.high）
????????return?a

def?do_episode（policy?env?num_steps?render=False）:
????total_rew?=?0
????ob?=?env.reset（）
????for?t?in?range（num_steps）:
????????a?=?policy.act（ob）
????????（ob?reward?done?_info）?=?env.step（a）
????????total_rew?+=?reward
????????if?render?and?t%3==0:?env.render（）
????????if?done:?break
????return?total_rew

env?=?None
def?noisy_evaluation（theta）:
????policy?=?make_policy（theta）
????rew?=?do_episode（policy?env?num_steps）
????return?rew

def?make_policy（theta）:
????if?isinstance（env.action_space?Discrete）:
????????return?DeterministicDiscreteActionLinearPolicy（theta
????????????env.observation_space?env.action_space）
????elif?isinstance（env.action_space?Box）:
????????return?DeterministicContinuousActionLinearPolicy（theta
????????????env.observation_space?env.action_space）
????else:
????????raise?NotImplementedError

#?Task?settings:
#?env?=?gym.make（‘CartPole-v0‘）
env?=?gym.make（‘MountainCar-v0‘）
#?Change?as?needed
num_steps?=?500?#?maximum?length?of?episode

#?Alg?settings:
n_iter?=?100?#?number?of?iterations?of?CEM
batch_size?=?25?#?number?of?samples?per?batch
elite_frac?=?0.2?#?fraction?of?samples?used?as?elite?set

if?isinstance（env.action_space?Discrete）:
????dim_theta?=?（env.observation_space.shape[0]+1）?*?env.action_space.n
elif?isinstance（env.action_space?Box）:
????dim_theta?=?（env.observation_space.shape[0]+1）?*?env.action_space.shape[0]
else:
????raise?NotImplementedError

#?Initialize?me

?屬性????????????大小?????日期????時間???名稱
-----------?---------??----------?-----??----
?????目錄???????????0??2018-03-08?09:44??CEM_PolicyGradient_gym\
?????目錄???????????0??2018-03-08?09:42??CEM_PolicyGradient_gym\.idea\
?????文件?????????455??2018-03-08?09:42??CEM_PolicyGradient_gym\.idea\CEM_Implementation.iml
?????文件?????????212??2018-03-08?09:42??CEM_PolicyGradient_gym\.idea\misc.xml
?????文件?????????288??2018-03-08?09:42??CEM_PolicyGradient_gym\.idea\modules.xml
?????文件???????13488??2018-03-08?09:42??CEM_PolicyGradient_gym\.idea\workspace.xml
?????文件????????4393??2018-03-08?09:42??CEM_PolicyGradient_gym\CEM.py
?????文件????????6727??2018-03-08?09:42??CEM_PolicyGradient_gym\Policy_Gradient.py
?????文件??????????88??2018-03-08?09:45??CEM_PolicyGradient_gym\readme.txt