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SELECT.py
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SELECT.py
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# -*- coding: utf-8 -*-
"""
Created on Wed Apr 24 11:35:05 2019
@author: wangjingyi
"""
import sys
import gym
import numpy as np
import tensorflow as tf
sys.path.append('./')
sys.path.append('model')
from Util import Memory ,StateProcessor
from DDPG import DDPG
from ACNetwork import ACNetwork
np.random.seed(1)
tf.set_random_seed(1)
import time
import logging # 引入logging模块
logger = logging.getLogger(__name__)
logging.basicConfig(level=logging.DEBUG,
format='%(asctime)s - %(filename)s[line:%(lineno)d] - %(levelname)s: %(message)s') # logging.basicConfig函数对日志的输出格式及方式做相关配置
logger.setLevel(level = logging.INFO)
handler = logging.FileHandler("Pendulum/log/log_" + str(time.time()) + '.txt')
handler.setLevel(logging.INFO)
formatter = logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s')
handler.setFormatter(formatter)
logger.addHandler(handler)
console = logging.StreamHandler()
console.setLevel(logging.INFO)
logger.addHandler(console)
# 由于日志基本配置中级别设置为DEBUG，所以一下打印信息将会全部显示在控制台上
import os
os.environ["CUDA_VISIBLE_DEVICES"] = "0"
tfconfig = tf.ConfigProto()
tfconfig.gpu_options.allow_growth = True
session = tf.Session(config=tfconfig)
class DDPG4Pendulum(DDPG):
"""docstring for ClassName"""
def __init__(self, **kwargs):
super(DDPG4Pendulum, self).__init__(**kwargs)
def _build_a_net(self,s,scope,trainable):
#w_initializer, b_initializer = tf.random_normal_initializer(0., 0.3), tf.constant_initializer(0.1)
w_initializer, b_initializer = None,None
with tf.variable_scope(scope):
e1 = tf.layers.dense(inputs=s,
units=30,
bias_initializer = b_initializer,
kernel_initializer=w_initializer,
activation = tf.nn.relu,
trainable=trainable)
a = tf.layers.dense(inputs=e1,
units=self.n_actions,
bias_initializer = b_initializer,
kernel_initializer=w_initializer,
activation = tf.nn.tanh,
trainable=trainable)
return tf.multiply(a, self.a_bound, name='scaled_a')
def _build_c_net(self, s, a, scope, trainable):
with tf.variable_scope(scope):
n_l1 = 30
w1_s = tf.get_variable('w1_s', [self.n_features, n_l1], trainable=trainable)
w1_a = tf.get_variable('w1_a', [self.n_actions, n_l1], trainable=trainable)
b1 = tf.get_variable('b1', [1, n_l1], trainable=trainable)
net = tf.nn.relu(tf.matmul(s, w1_s) + tf.matmul(a, w1_a) + b1)
return tf.layers.dense(net, 1, trainable=trainable) # Q(s,a)
# def _build_c_net(self,s,a,scope,trainable):
# #trainable = True if reuse is None else False
# w_initializer, b_initializer = tf.random_normal_initializer(0., 0.3), tf.constant_initializer(0.1)
# #with tf.variable_scope('Critic',reuse = reuse,custom_getter=custom_getter):
# with tf.variable_scope(scope):
# s1 = tf.layers.dense(inputs=s,
# units=32,
# bias_initializer = b_initializer,
# kernel_initializer=w_initializer,
# activation = tf.nn.relu,
# trainable=trainable)
# a1 = tf.layers.dense(inputs=a,
# units=32,
# bias_initializer = b_initializer,
# kernel_initializer=w_initializer,
# activation = tf.nn.relu,
# trainable=trainable)
# h_dense = s1+a1#tf.concat([s1, a1], axis=1, name="h_concat")
# # h_dense = tf.layers.dense(inputs=h_dense,
# # units=16,
# # bias_initializer = b_initializer,
# # kernel_initializer=w_initializer,
# # activation = tf.nn.relu,
# # trainable=trainable)
# q = tf.layers.dense(inputs=h_dense,
# units=1,
# bias_initializer = b_initializer,
# kernel_initializer=w_initializer,
# activation = tf.nn.relu,
# trainable=trainable)
# return q
##################### hyper parameters ####################
MAX_EPISODES = 50000
MAX_EP_STEPS = 500
batch_size = 32
RENDER = True
ENV_NAME = 'Pendulum-v0'
env = gym.make(ENV_NAME)
env = env.unwrapped
env.seed(1)
print('env.observation_space: ' + str(env.observation_space))
print('env.action_space: ' + str(env.action_space))
n_features = env.observation_space.shape[0]
n_actions = env.action_space.shape[0]
a_bound = env.action_space.high
memory_size = 10000
logger.info('n_features: ' + str(n_features))
logger.info('n_actions: ' + str(n_actions))
logger.info('a_bound: ' + str(a_bound))
############################### training ####################################
ddpg = DDPG4Pendulum(n_actions=n_actions,
n_features=n_features,
reward_decay=0.9,
lr_a = 0.001,
lr_c = 0.002,
TAU = 0.01,
output_graph=False,
log_dir = 'Pendulum/log/DDPG4Pendulum/',
a_bound =a_bound,
model_dir = 'Pendulum/model_dir/DDPG4Pendulum/')
memory = Memory(memory_size=memory_size)
var = 3 # control exploration
t1 = time.time()
step = 0
for i in range(MAX_EPISODES):
s = env.reset()
logger.info('env.reset() s: ' + str(s))
ep_reward = 0
logger.debug('env s:')
logger.debug(s)
for j in range(MAX_EP_STEPS):
step+=1
if RENDER:
env.render()
# Add exploration noise
a = ddpg.choose_action(s)
logger.info('action choose: ' + str(a))
a = np.clip(np.random.normal(a, var), -2, 2) # add randomness to action selection for exploration
logger.info('var action choose: ' + str(a))
s_, r, done, info = env.step(a)
memory.store_transition(s, a, r / 10, s_)
logger.info('memory.store_transition: ' + str(s) + ' ,' + str(a) + ' ,' + str(r/10) + ' ,' + str(s_))
if step > memory_size:
#env.render()
logger.info('-----------DDPG4Pendulum learning :-----------------' + str(step))
var *= .9995 # decay the action randomness
logger.info('learn var: ' + str(var))
data = memory.sample(batch_size)
# logger.info('learn data: ' + str(data))
ddpg.learn(data)
s = s_
ep_reward += r
if j == MAX_EP_STEPS-1:
logger.info('Episode:', i, ' Reward: %i' % int(ep_reward), 'Explore: %.2f' % var, )
# if ep_reward > -300:RENDER = True
break
logger.info('Running time: ', time.time() - t1)