>>>>>># training >>>>>>>#考研政治冲刺env = gym.make(ENV_NAME)
env = env.unwrapped
env.ed(1)
s_dim = env.obrvation_space.shape[0]成都信息工程学院录取查询
a_dim = env.action_space.shape[0]
a_bound = env.action_space.high
ddpg = DDPG(a_dim, s_dim, a_bound)
var =3# control exploration
t1 = time.time()
for i in range(MAX_EPISODES):
s = ()
ep_reward =0
for j in range(MAX_EP_STEPS):
if RENDER:
# Add exploration noi
a = ddpg.choo_action(s)
a = np.clip(al(a, var),-2,2)# add randomness to action lection for exploration s_, r, done, info = env.step(a)
ddpg.store_transition(s, a, r /10, s_)法律英语
if ddpg.pointer > MEMORY_CAPACITY:
var *=.9995# decay the action randomness
ddpg.learn()
s = s_
ep_reward += r
if j == MAX_EP_STEPS-1:
print('Episode:', i,' Reward: %i'%int(ep_reward),'Explore: %.2f'% var,)
if ep_reward >-300:RENDER =True
break
print('Running time: ', time.time()- t1)
效果如图所⽰：
基于进化算法的强化学习：
"""
According to morvanzhou.github.io/tutorials/
required pytorch=0.41
"""
import numpy as np
import gym
import multiprocessing as mp
import time
import torch
as nn
functional as F
N_KID =10# half of the training population
N_GENERATION =5000# training step
LR =.05# learning rate
SIGMA =.05# mutation strength or step size
N_CORE = mp.cpu_count()-1
CONFIG =[
dict(game="CartPole-v0",
四月英文缩写n_feature=4, n_action=2, continuous_a=[Fal], ep_max_step=700, eval_threshold=500), dict(game="MountainCar-v0",
n_feature=2, n_action=3, continuous_a=[Fal], ep_max_step=200, eval_threshold=-120), dict(game="Pendulum-v0",
宝贝鱼在线翻译n_feature=3, n_action=1, continuous_a=[True,2.], ep_max_step=200, eval_threshold=-180) ][0]# choo your game
class net(nn.Module):
def__init__(lf,input_dim,output_dim):
super(net,lf).__init__()
lf.fc1 = nn.Linear(input_dim,30)
lf.fc1.al_(0,1)
lf.fc2 = nn.Linear(30,20)
lf.fc2.al_(0,1)
lf.fc3 = nn.Linear(20,output_dim)
lf.fc3 = nn.Linear(20,output_dim)
lf.fc3.al_(0,1)
def forward(lf,x):
x = F.tanh(lf.fc1(x))
x = F.tanh(lf.fc2(x))
out = lf.fc3(x)
return out
def sign(k_id):return-1.if k_id %2==0el1.# mirrored sampling
class SGD(object):# optimizer with momentum
def__init__(lf, params, learning_rate, momentum=0.9):
lf.v = np.zeros(params).astype(np.float32)
lf.lr, lf.momentum = learning_rate, momentum
def get_gradients(lf, gradients):
lf.v = lf.momentum * lf.v +(1.- lf.momentum)* gradients
return lf.lr * lf.v
def get_reward(network_param, num_p,env, ep_max_step, continuous_a, ed_and_id=None,):
# perturb parameters using ed
if ed_and_id is not None:
ed, k_id = ed_and_id
# for layer in network.children():
# np.random.ed(ed)
# layer.weight.data += torch.FloatTensor(sign(k_id) * SIGMA * np.random.randn(layer.weight.shape[0],layer.weight.shape[1]))
# np.random.ed(ed)
# layer.bias.data += torch.FloatTensor(sign(k_id) * SIGMA * np.random.randn(layer.bias.shape[0]))
np.random.ed(ed)
params = torch.FloatTensor(sign(k_id)* SIGMA * np.random.randn(num_p))
Net = net(CONFIG['n_feature'],CONFIG['n_action'])
Net.load_state_dict(network_param)
for layer in Net.children():
layer.weight.data += params[:layer.weight.shape[0]*layer.weight.shape[1]].view(layer.weight.shape[0],layer.weight.shape[1])
layer.bias.data += params[layer.weight.shape[0]*layer.weight.shape[1]:layer.bias.shape[0]+layer.weight.shape[0]*layer.weight.shape[1]] params = params[layer.bias.shape[0]+layer.weight.shape[0]*layer.weight.shape[1]:]
el:
Net = net(CONFIG['n_feature'], CONFIG['n_action'])
Net.load_state_dict(network_param)
# run episode
s = ()
ep_r =0.
for step in range(ep_max_step):
a = get_action(Net, s, continuous_a)# continuous_a 动作是否连续
s, r, done, _ = env.step(a)
# mountain car's reward can be tricky
中国好声音插曲if env.spec._env_name =='MountainCar'and s[0]>-0.1: r =0.
ep_r += r
if done:break
return ep_r
def get_action(network, x, continuous_a):
x = torch.unsqueeze(torch.FloatTensor(x),0)
x = network.forward(x)
if not continuous_a[0]:return np.argmax(x.detach().numpy(), axis=1)[0]# for discrete action
el:return continuous_a[1]* np.tanh(x.detach().numpy())[0]# for continuous action
def train(network_param, num_p,optimizer, utility, pool):
# pass ed instead whole noi matrix to parallel will save your time
noi_ed = np.random.randint(0,2**32-1, size=N_KID, dtype=np.uint32).repeat(2)# mirrored sampling
# ⽣成⼀些镜像的噪点，每⼀个种群⼀个噪点ed
