save ddpg model in database
This commit is contained in:
Dongsheng Yang
committed by
Dana Van Aken
Dana Van Aken
parent
c8fbaf6e4b
commit
a3fcf59f07
@@ -1,10 +1,10 @@
|
||||
#
|
||||
# __init__.py
|
||||
#
|
||||
# Copyright
|
||||
#
|
||||
|
||||
|
||||
from analysis.ddpg.ddpg import DDPG
|
||||
|
||||
__all__ = ["DDPG"]
|
||||
#
|
||||
# OtterTune - __init__.py
|
||||
#
|
||||
# Copyright (c) 2017-18, Carnegie Mellon University Database Group
|
||||
#
|
||||
|
||||
|
||||
from analysis.ddpg.ddpg import DDPG
|
||||
|
||||
__all__ = ["DDPG"]
|
||||
|
||||
@@ -1,509 +1,428 @@
|
||||
#
|
||||
# ddpg.py
|
||||
#
|
||||
# Copyright
|
||||
#
|
||||
"""
|
||||
Deep Deterministic Policy Gradient Model
|
||||
|
||||
"""
|
||||
|
||||
import logging
|
||||
import os
|
||||
import sys
|
||||
import math
|
||||
import pickle
|
||||
import numpy as np
|
||||
import torch
|
||||
import torch.nn as nn
|
||||
from torch.nn import init, Parameter
|
||||
import torch.nn.functional as F
|
||||
import torch.optim as optimizer
|
||||
from torch.autograd import Variable
|
||||
|
||||
from analysis.ddpg.OUProcess import OUProcess
|
||||
from analysis.ddpg.prioritized_replay_memory import PrioritizedReplayMemory
|
||||
|
||||
LOG = logging.getLogger(__name__)
|
||||
|
||||
sys.path.append('../')
|
||||
|
||||
|
||||
# code from https://github.com/Kaixhin/NoisyNet-A3C/blob/master/model.py
|
||||
class NoisyLinear(nn.Linear):
|
||||
def __init__(self, in_features, out_features, sigma_init=0.05, bias=True):
|
||||
super(NoisyLinear, self).__init__(in_features, out_features, bias=True)
|
||||
# reuse self.weight and self.bias
|
||||
self.sigma_init = sigma_init
|
||||
self.sigma_weight = Parameter(torch.Tensor(out_features, in_features))
|
||||
self.sigma_bias = Parameter(torch.Tensor(out_features))
|
||||
self.register_buffer('epsilon_weight', torch.zeros(out_features, in_features))
|
||||
self.register_buffer('epsilon_bias', torch.zeros(out_features))
|
||||
self.reset_parameters()
|
||||
|
||||
def reset_parameters(self):
|
||||
# Only init after all params added (otherwise super().__init__() fails)
|
||||
if hasattr(self, 'sigma_weight'):
|
||||
init.uniform(self.weight, -math.sqrt(3 / self.in_features),
|
||||
math.sqrt(3 / self.in_features))
|
||||
init.uniform(self.bias, -math.sqrt(3 / self.in_features),
|
||||
math.sqrt(3 / self.in_features))
|
||||
init.constant(self.sigma_weight, self.sigma_init)
|
||||
init.constant(self.sigma_bias, self.sigma_init)
|
||||
|
||||
def forward(self, x):
|
||||
return F.linear(x, self.weight + self.sigma_weight * Variable(self.epsilon_weight),
|
||||
self.bias + self.sigma_bias * Variable(self.epsilon_bias))
|
||||
|
||||
# pylint: disable=attribute-defined-outside-init
|
||||
def sample_noise(self):
|
||||
self.epsilon_weight = torch.randn(self.out_features, self.in_features)
|
||||
self.epsilon_bias = torch.randn(self.out_features)
|
||||
|
||||
def remove_noise(self):
|
||||
self.epsilon_weight = torch.zeros(self.out_features, self.in_features)
|
||||
self.epsilon_bias = torch.zeros(self.out_features)
|
||||
# pylint: enable=attribute-defined-outside-init
|
||||
|
||||
|
||||
class Normalizer(object):
|
||||
|
||||
def __init__(self, mean, variance):
|
||||
if isinstance(mean, list):
|
||||
mean = np.array(mean)
|
||||
if isinstance(variance, list):
|
||||
variance = np.array(variance)
|
||||
self.mean = mean
|
||||
self.std = np.sqrt(variance + 0.00001)
|
||||
|
||||
def normalize(self, x):
|
||||
if isinstance(x, list):
|
||||
x = np.array(x)
|
||||
x = x - self.mean
|
||||
x = x / self.std
|
||||
|
||||
return Variable(torch.FloatTensor(x))
|
||||
|
||||
def __call__(self, x, *args, **kwargs):
|
||||
return self.normalize(x)
|
||||
|
||||
|
||||
class ActorLow(nn.Module):
|
||||
|
||||
def __init__(self, n_states, n_actions, ):
|
||||
super(ActorLow, self).__init__()
|
||||
self.layers = nn.Sequential(
|
||||
nn.BatchNorm1d(n_states),
|
||||
nn.Linear(n_states, 32),
|
||||
nn.LeakyReLU(negative_slope=0.2),
|
||||
nn.BatchNorm1d(32),
|
||||
nn.Linear(32, n_actions),
|
||||
nn.LeakyReLU(negative_slope=0.2)
|
||||
)
|
||||
self._init_weights()
|
||||
self.out_func = nn.Tanh()
|
||||
|
||||
def _init_weights(self):
|
||||
|
||||
for m in self.layers:
|
||||
if isinstance(m, nn.Linear):
|
||||
m.weight.data.normal_(0.0, 1e-3)
|
||||
m.bias.data.uniform_(-0.1, 0.1)
|
||||
|
||||
def forward(self, x): # pylint: disable=arguments-differ
|
||||
|
||||
out = self.layers(x)
|
||||
|
||||
return self.out_func(out)
|
||||
|
||||
|
||||
class CriticLow(nn.Module):
|
||||
|
||||
def __init__(self, n_states, n_actions):
|
||||
super(CriticLow, self).__init__()
|
||||
self.state_input = nn.Linear(n_states, 32)
|
||||
self.action_input = nn.Linear(n_actions, 32)
|
||||
self.act = nn.LeakyReLU(negative_slope=0.2)
|
||||
self.state_bn = nn.BatchNorm1d(n_states)
|
||||
self.layers = nn.Sequential(
|
||||
nn.Linear(64, 1),
|
||||
nn.LeakyReLU(negative_slope=0.2),
|
||||
)
|
||||
self._init_weights()
|
||||
|
||||
def _init_weights(self):
|
||||
self.state_input.weight.data.normal_(0.0, 1e-3)
|
||||
self.state_input.bias.data.uniform_(-0.1, 0.1)
|
||||
|
||||
self.action_input.weight.data.normal_(0.0, 1e-3)
|
||||
self.action_input.bias.data.uniform_(-0.1, 0.1)
|
||||
|
||||
for m in self.layers:
|
||||
if isinstance(m, nn.Linear):
|
||||
m.weight.data.normal_(0.0, 1e-3)
|
||||
m.bias.data.uniform_(-0.1, 0.1)
|
||||
|
||||
def forward(self, x, action): # pylint: disable=arguments-differ
|
||||
x = self.state_bn(x)
|
||||
x = self.act(self.state_input(x))
|
||||
action = self.act(self.action_input(action))
|
||||
|
||||
_input = torch.cat([x, action], dim=1)
|
||||
value = self.layers(_input)
|
||||
return value
|
||||
|
||||
|
||||
class Actor(nn.Module):
|
||||
|
||||
def __init__(self, n_states, n_actions, noisy=False):
|
||||
super(Actor, self).__init__()
|
||||
self.layers = nn.Sequential(
|
||||
nn.Linear(n_states, 128),
|
||||
nn.LeakyReLU(negative_slope=0.2),
|
||||
nn.BatchNorm1d(128),
|
||||
nn.Linear(128, 128),
|
||||
nn.Tanh(),
|
||||
nn.Dropout(0.3),
|
||||
|
||||
nn.Linear(128, 64),
|
||||
nn.Tanh(),
|
||||
nn.BatchNorm1d(64),
|
||||
)
|
||||
if noisy:
|
||||
self.out = NoisyLinear(64, n_actions)
|
||||
else:
|
||||
self.out = nn.Linear(64, n_actions)
|
||||
self._init_weights()
|
||||
self.act = nn.Sigmoid()
|
||||
|
||||
def _init_weights(self):
|
||||
|
||||
for m in self.layers:
|
||||
if isinstance(m, nn.Linear):
|
||||
m.weight.data.normal_(0.0, 1e-2)
|
||||
m.bias.data.uniform_(-0.1, 0.1)
|
||||
|
||||
def sample_noise(self):
|
||||
self.out.sample_noise()
|
||||
|
||||
def forward(self, x): # pylint: disable=arguments-differ
|
||||
|
||||
out = self.act(self.out(self.layers(x)))
|
||||
return out
|
||||
|
||||
|
||||
class Critic(nn.Module):
|
||||
|
||||
def __init__(self, n_states, n_actions):
|
||||
super(Critic, self).__init__()
|
||||
self.state_input = nn.Linear(n_states, 128)
|
||||
self.action_input = nn.Linear(n_actions, 128)
|
||||
self.act = nn.Tanh()
|
||||
self.layers = nn.Sequential(
|
||||
nn.Linear(256, 256),
|
||||
nn.LeakyReLU(negative_slope=0.2),
|
||||
nn.BatchNorm1d(256),
|
||||
|
||||
nn.Linear(256, 64),
|
||||
nn.Tanh(),
|
||||
nn.Dropout(0.3),
|
||||
nn.BatchNorm1d(64),
|
||||
nn.Linear(64, 1),
|
||||
)
|
||||
self._init_weights()
|
||||
|
||||
def _init_weights(self):
|
||||
self.state_input.weight.data.normal_(0.0, 1e-2)
|
||||
self.state_input.bias.data.uniform_(-0.1, 0.1)
|
||||
|
||||
self.action_input.weight.data.normal_(0.0, 1e-2)
|
||||
self.action_input.bias.data.uniform_(-0.1, 0.1)
|
||||
|
||||
for m in self.layers:
|
||||
if isinstance(m, nn.Linear):
|
||||
m.weight.data.normal_(0.0, 1e-2)
|
||||
m.bias.data.uniform_(-0.1, 0.1)
|
||||
|
||||
def forward(self, x, action): # pylint: disable=arguments-differ
|
||||
x = self.act(self.state_input(x))
|
||||
action = self.act(self.action_input(action))
|
||||
|
||||
_input = torch.cat([x, action], dim=1)
|
||||
value = self.layers(_input)
|
||||
return value
|
||||
|
||||
|
||||
class DDPG(object):
|
||||
|
||||
def __init__(self, n_states, n_actions, opt=None, ouprocess=True, mean_var_path=None,
|
||||
supervised=False):
|
||||
""" DDPG Algorithms
|
||||
Args:
|
||||
n_states: int, dimension of states
|
||||
n_actions: int, dimension of actions
|
||||
opt: dict, params
|
||||
supervised, bool, pre-train the actor with supervised learning
|
||||
"""
|
||||
self.n_states = n_states
|
||||
self.n_actions = n_actions
|
||||
|
||||
if opt is None:
|
||||
opt = {
|
||||
'model': '',
|
||||
'alr': 0.001,
|
||||
'clr': 0.001,
|
||||
'gamma': 0.9,
|
||||
'batch_size': 32,
|
||||
'tau': 0.002,
|
||||
'memory_size': 100000
|
||||
}
|
||||
|
||||
# Params
|
||||
self.alr = opt['alr']
|
||||
self.clr = opt['clr']
|
||||
self.model_name = opt['model']
|
||||
self.batch_size = opt['batch_size']
|
||||
self.gamma = opt['gamma']
|
||||
self.tau = opt['tau']
|
||||
self.ouprocess = ouprocess
|
||||
|
||||
if mean_var_path is None:
|
||||
mean = np.zeros(n_states)
|
||||
var = np.zeros(n_states)
|
||||
elif not os.path.exists(mean_var_path):
|
||||
mean = np.zeros(n_states)
|
||||
var = np.zeros(n_states)
|
||||
else:
|
||||
with open(mean_var_path, 'rb') as f:
|
||||
mean, var = pickle.load(f)
|
||||
|
||||
self.normalizer = Normalizer(mean, var)
|
||||
|
||||
if supervised:
|
||||
self._build_actor()
|
||||
LOG.info("Supervised Learning Initialized")
|
||||
else:
|
||||
# Build Network
|
||||
self._build_network()
|
||||
LOG.info('Finish Initializing Networks')
|
||||
|
||||
self.replay_memory = PrioritizedReplayMemory(capacity=opt['memory_size'])
|
||||
self.noise = OUProcess(n_actions)
|
||||
# LOG.info('DDPG Initialzed!')
|
||||
|
||||
@staticmethod
|
||||
def totensor(x):
|
||||
return Variable(torch.FloatTensor(x))
|
||||
|
||||
def _build_actor(self):
|
||||
if self.ouprocess:
|
||||
noisy = False
|
||||
else:
|
||||
noisy = True
|
||||
self.actor = Actor(self.n_states, self.n_actions, noisy=noisy)
|
||||
self.actor_criterion = nn.MSELoss()
|
||||
self.actor_optimizer = optimizer.Adam(lr=self.alr, params=self.actor.parameters())
|
||||
|
||||
def _build_network(self):
|
||||
if self.ouprocess:
|
||||
noisy = False
|
||||
else:
|
||||
noisy = True
|
||||
self.actor = Actor(self.n_states, self.n_actions, noisy=noisy)
|
||||
self.target_actor = Actor(self.n_states, self.n_actions)
|
||||
self.critic = Critic(self.n_states, self.n_actions)
|
||||
self.target_critic = Critic(self.n_states, self.n_actions)
|
||||
|
||||
# if model params are provided, load them
|
||||
if len(self.model_name):
|
||||
self.load_model(model_name=self.model_name)
|
||||
LOG.info("Loading model from file: %s", self.model_name)
|
||||
|
||||
# Copy actor's parameters
|
||||
self._update_target(self.target_actor, self.actor, tau=1.0)
|
||||
|
||||
# Copy critic's parameters
|
||||
self._update_target(self.target_critic, self.critic, tau=1.0)
|
||||
|
||||
self.loss_criterion = nn.MSELoss()
|
||||
self.actor_optimizer = optimizer.Adam(lr=self.alr, params=self.actor.parameters(),
|
||||
weight_decay=1e-5)
|
||||
self.critic_optimizer = optimizer.Adam(lr=self.clr, params=self.critic.parameters(),
|
||||
weight_decay=1e-5)
|
||||
|
||||
@staticmethod
|
||||
def _update_target(target, source, tau):
|
||||
for (target_param, param) in zip(target.parameters(), source.parameters()):
|
||||
target_param.data.copy_(
|
||||
target_param.data * (1 - tau) + param.data * tau
|
||||
)
|
||||
|
||||
def reset(self, sigma):
|
||||
self.noise.reset(sigma)
|
||||
|
||||
def _sample_batch(self):
|
||||
batch, idx = self.replay_memory.sample(self.batch_size)
|
||||
# batch = self.replay_memory.sample(self.batch_size)
|
||||
states = list(map(lambda x: x[0].tolist(), batch)) # pylint: disable=W0141
|
||||
next_states = list(map(lambda x: x[3].tolist(), batch)) # pylint: disable=W0141
|
||||
actions = list(map(lambda x: x[1].tolist(), batch)) # pylint: disable=W0141
|
||||
rewards = list(map(lambda x: x[2], batch)) # pylint: disable=W0141
|
||||
terminates = list(map(lambda x: x[4], batch)) # pylint: disable=W0141
|
||||
|
||||
return idx, states, next_states, actions, rewards, terminates
|
||||
|
||||
def add_sample(self, state, action, reward, next_state, terminate):
|
||||
self.critic.eval()
|
||||
self.actor.eval()
|
||||
self.target_critic.eval()
|
||||
self.target_actor.eval()
|
||||
batch_state = self.normalizer([state.tolist()])
|
||||
batch_next_state = self.normalizer([next_state.tolist()])
|
||||
current_value = self.critic(batch_state, self.totensor([action.tolist()]))
|
||||
target_action = self.target_actor(batch_next_state)
|
||||
target_value = self.totensor([reward]) \
|
||||
+ self.totensor([0 if x else 1 for x in [terminate]]) \
|
||||
* self.target_critic(batch_next_state, target_action) * self.gamma
|
||||
error = float(torch.abs(current_value - target_value).data.numpy()[0])
|
||||
|
||||
self.target_actor.train()
|
||||
self.actor.train()
|
||||
self.critic.train()
|
||||
self.target_critic.train()
|
||||
self.replay_memory.add(error, (state, action, reward, next_state, terminate))
|
||||
|
||||
def update(self):
|
||||
idxs, states, next_states, actions, rewards, terminates = self._sample_batch()
|
||||
batch_states = self.normalizer(states)
|
||||
batch_next_states = self.normalizer(next_states)
|
||||
batch_actions = self.totensor(actions)
|
||||
batch_rewards = self.totensor(rewards)
|
||||
mask = [0 if x else 1 for x in terminates]
|
||||
mask = self.totensor(mask)
|
||||
|
||||
target_next_actions = self.target_actor(batch_next_states).detach()
|
||||
target_next_value = self.target_critic(batch_next_states, target_next_actions).detach()
|
||||
current_value = self.critic(batch_states, batch_actions)
|
||||
# TODO (dongshen): This clause is the original clause, but it has some mistakes
|
||||
# next_value = batch_rewards + mask * target_next_value * self.gamma
|
||||
# Since terminate is always false, I remove the mask here.
|
||||
next_value = batch_rewards + target_next_value * self.gamma
|
||||
# Update Critic
|
||||
|
||||
# update prioritized memory
|
||||
error = torch.abs(current_value - next_value).data.numpy()
|
||||
for i in range(self.batch_size):
|
||||
idx = idxs[i]
|
||||
self.replay_memory.update(idx, error[i][0])
|
||||
|
||||
loss = self.loss_criterion(current_value, next_value)
|
||||
self.critic_optimizer.zero_grad()
|
||||
loss.backward()
|
||||
self.critic_optimizer.step()
|
||||
|
||||
# Update Actor
|
||||
self.critic.eval()
|
||||
policy_loss = -self.critic(batch_states, self.actor(batch_states))
|
||||
policy_loss = policy_loss.mean()
|
||||
self.actor_optimizer.zero_grad()
|
||||
policy_loss.backward()
|
||||
|
||||
self.actor_optimizer.step()
|
||||
self.critic.train()
|
||||
|
||||
self._update_target(self.target_critic, self.critic, tau=self.tau)
|
||||
self._update_target(self.target_actor, self.actor, tau=self.tau)
|
||||
|
||||
return loss.data, policy_loss.data
|
||||
|
||||
def choose_action(self, x):
|
||||
""" Select Action according to the current state
|
||||
Args:
|
||||
x: np.array, current state
|
||||
"""
|
||||
self.actor.eval()
|
||||
act = self.actor(self.normalizer([x.tolist()])).squeeze(0)
|
||||
self.actor.train()
|
||||
action = act.data.numpy()
|
||||
if self.ouprocess:
|
||||
action += self.noise.noise()
|
||||
return action.clip(0, 1)
|
||||
|
||||
def sample_noise(self):
|
||||
self.actor.sample_noise()
|
||||
|
||||
def load_model(self, model_name):
|
||||
""" Load Torch Model from files
|
||||
Args:
|
||||
model_name: str, model path
|
||||
"""
|
||||
self.actor.load_state_dict(
|
||||
torch.load('{}_actor.pth'.format(model_name))
|
||||
)
|
||||
self.critic.load_state_dict(
|
||||
torch.load('{}_critic.pth'.format(model_name))
|
||||
)
|
||||
|
||||
def save_model(self, model_name):
|
||||
""" Save Torch Model from files
|
||||
Args:
|
||||
model_dir: str, model dir
|
||||
title: str, model name
|
||||
"""
|
||||
torch.save(
|
||||
self.actor.state_dict(),
|
||||
'{}_actor.pth'.format(model_name)
|
||||
)
|
||||
|
||||
torch.save(
|
||||
self.critic.state_dict(),
|
||||
'{}_critic.pth'.format(model_name)
|
||||
)
|
||||
|
||||
def save_actor(self, path):
|
||||
""" save actor network
|
||||
Args:
|
||||
path, str, path to save
|
||||
"""
|
||||
torch.save(
|
||||
self.actor.state_dict(),
|
||||
path
|
||||
)
|
||||
|
||||
def load_actor(self, path):
|
||||
""" load actor network
|
||||
Args:
|
||||
path, str, path to load
|
||||
"""
|
||||
self.actor.load_state_dict(
|
||||
torch.load(path)
|
||||
)
|
||||
|
||||
def train_actor(self, batch_data, is_train=True):
|
||||
""" Train the actor separately with data
|
||||
Args:
|
||||
batch_data: tuple, (states, actions)
|
||||
is_train: bool
|
||||
Return:
|
||||
_loss: float, training loss
|
||||
"""
|
||||
states, action = batch_data
|
||||
|
||||
if is_train:
|
||||
self.actor.train()
|
||||
pred = self.actor(self.normalizer(states))
|
||||
action = self.totensor(action)
|
||||
|
||||
_loss = self.actor_criterion(pred, action)
|
||||
|
||||
self.actor_optimizer.zero_grad()
|
||||
_loss.backward()
|
||||
self.actor_optimizer.step()
|
||||
|
||||
else:
|
||||
self.actor.eval()
|
||||
pred = self.actor(self.normalizer(states))
|
||||
action = self.totensor(action)
|
||||
_loss = self.actor_criterion(pred, action)
|
||||
|
||||
return _loss.data[0]
|
||||
#
|
||||
# OtterTune - ddpg.py
|
||||
#
|
||||
# Copyright (c) 2017-18, Carnegie Mellon University Database Group
|
||||
#
|
||||
# from: https://github.com/KqSMea8/CDBTune
|
||||
# Zhang, Ji, et al. "An end-to-end automatic cloud database tuning system using
|
||||
# deep reinforcement learning." Proceedings of the 2019 International Conference
|
||||
# on Management of Data. ACM, 2019
|
||||
|
||||
import os
|
||||
import pickle
|
||||
import math
|
||||
import numpy as np
|
||||
import torch
|
||||
import torch.nn as nn
|
||||
from torch.nn import init, Parameter
|
||||
import torch.nn.functional as F
|
||||
import torch.optim as optimizer
|
||||
from torch.autograd import Variable
|
||||
|
||||
from analysis.ddpg.ou_process import OUProcess
|
||||
from analysis.ddpg.prioritized_replay_memory import PrioritizedReplayMemory
|
||||
from analysis.util import get_analysis_logger
|
||||
|
||||
LOG = get_analysis_logger(__name__)
|
||||
|
||||
|
||||
# code from https://github.com/Kaixhin/NoisyNet-A3C/blob/master/model.py
|
||||
class NoisyLinear(nn.Linear):
|
||||
def __init__(self, in_features, out_features, sigma_init=0.05, bias=True):
|
||||
super(NoisyLinear, self).__init__(in_features, out_features, bias=True)
|
||||
# reuse self.weight and self.bias
|
||||
self.sigma_init = sigma_init
|
||||
self.sigma_weight = Parameter(torch.Tensor(out_features, in_features))
|
||||
self.sigma_bias = Parameter(torch.Tensor(out_features))
|
||||
self.epsilon_weight = None
|
||||
self.epsilon_bias = None
|
||||
self.register_buffer('epsilon_weight', torch.zeros(out_features, in_features))
|
||||
self.register_buffer('epsilon_bias', torch.zeros(out_features))
|
||||
self.reset_parameters()
|
||||
|
||||
def reset_parameters(self):
|
||||
# Only init after all params added (otherwise super().__init__() fails)
|
||||
if hasattr(self, 'sigma_weight'):
|
||||
init.uniform(self.weight, -math.sqrt(3 / self.in_features),
|
||||
math.sqrt(3 / self.in_features))
|
||||
init.uniform(self.bias, -math.sqrt(3 / self.in_features),
|
||||
math.sqrt(3 / self.in_features))
|
||||
init.constant(self.sigma_weight, self.sigma_init)
|
||||
init.constant(self.sigma_bias, self.sigma_init)
|
||||
|
||||
def forward(self, x):
|
||||
return F.linear(x, self.weight + self.sigma_weight * Variable(self.epsilon_weight),
|
||||
self.bias + self.sigma_bias * Variable(self.epsilon_bias))
|
||||
|
||||
def sample_noise(self):
|
||||
self.epsilon_weight = torch.randn(self.out_features, self.in_features)
|
||||
self.epsilon_bias = torch.randn(self.out_features)
|
||||
|
||||
def remove_noise(self):
|
||||
self.epsilon_weight = torch.zeros(self.out_features, self.in_features)
|
||||
self.epsilon_bias = torch.zeros(self.out_features)
|
||||
|
||||
|
||||
class Normalizer(object):
|
||||
|
||||
def __init__(self, mean, variance):
|
||||
if isinstance(mean, list):
|
||||
mean = np.array(mean)
|
||||
if isinstance(variance, list):
|
||||
variance = np.array(variance)
|
||||
self.mean = mean
|
||||
self.std = np.sqrt(variance + 0.00001)
|
||||
|
||||
def normalize(self, x):
|
||||
if isinstance(x, list):
|
||||
x = np.array(x)
|
||||
x = x - self.mean
|
||||
x = x / self.std
|
||||
|
||||
return Variable(torch.FloatTensor(x))
|
||||
|
||||
def __call__(self, x, *args, **kwargs):
|
||||
return self.normalize(x)
|
||||
|
||||
|
||||
class Actor(nn.Module):
|
||||
|
||||
def __init__(self, n_states, n_actions, noisy=False):
|
||||
super(Actor, self).__init__()
|
||||
self.layers = nn.Sequential(
|
||||
nn.Linear(n_states, 128),
|
||||
nn.LeakyReLU(negative_slope=0.2),
|
||||
nn.BatchNorm1d(128),
|
||||
nn.Linear(128, 128),
|
||||
nn.Tanh(),
|
||||
nn.Dropout(0.3),
|
||||
|
||||
nn.Linear(128, 64),
|
||||
nn.Tanh(),
|
||||
nn.BatchNorm1d(64),
|
||||
)
|
||||
if noisy:
|
||||
self.out = NoisyLinear(64, n_actions)
|
||||
else:
|
||||
self.out = nn.Linear(64, n_actions)
|
||||
self._init_weights()
|
||||
self.act = nn.Sigmoid()
|
||||
|
||||
def _init_weights(self):
|
||||
|
||||
for m in self.layers:
|
||||
if isinstance(m, nn.Linear):
|
||||
m.weight.data.normal_(0.0, 1e-2)
|
||||
m.bias.data.uniform_(-0.1, 0.1)
|
||||
|
||||
def sample_noise(self):
|
||||
self.out.sample_noise()
|
||||
|
||||
def forward(self, x): # pylint: disable=arguments-differ
|
||||
|
||||
out = self.act(self.out(self.layers(x)))
|
||||
return out
|
||||
|
||||
|
||||
class Critic(nn.Module):
|
||||
|
||||
def __init__(self, n_states, n_actions):
|
||||
super(Critic, self).__init__()
|
||||
self.state_input = nn.Linear(n_states, 128)
|
||||
self.action_input = nn.Linear(n_actions, 128)
|
||||
self.act = nn.Tanh()
|
||||
self.layers = nn.Sequential(
|
||||
nn.Linear(256, 256),
|
||||
nn.LeakyReLU(negative_slope=0.2),
|
||||
nn.BatchNorm1d(256),
|
||||
|
||||
nn.Linear(256, 64),
|
||||
nn.Tanh(),
|
||||
nn.Dropout(0.3),
|
||||
nn.BatchNorm1d(64),
|
||||
nn.Linear(64, 1),
|
||||
)
|
||||
self._init_weights()
|
||||
|
||||
def _init_weights(self):
|
||||
self.state_input.weight.data.normal_(0.0, 1e-2)
|
||||
self.state_input.bias.data.uniform_(-0.1, 0.1)
|
||||
|
||||
self.action_input.weight.data.normal_(0.0, 1e-2)
|
||||
self.action_input.bias.data.uniform_(-0.1, 0.1)
|
||||
|
||||
for m in self.layers:
|
||||
if isinstance(m, nn.Linear):
|
||||
m.weight.data.normal_(0.0, 1e-2)
|
||||
m.bias.data.uniform_(-0.1, 0.1)
|
||||
|
||||
def forward(self, x, action): # pylint: disable=arguments-differ
|
||||
x = self.act(self.state_input(x))
|
||||
action = self.act(self.action_input(action))
|
||||
|
||||
_input = torch.cat([x, action], dim=1)
|
||||
value = self.layers(_input)
|
||||
return value
|
||||
|
||||
|
||||
class DDPG(object):
|
||||
|
||||
def __init__(self, n_states, n_actions, model_name='', alr=0.001, clr=0.001,
|
||||
gamma=0.9, batch_size=32, tau=0.002, memory_size=100000,
|
||||
ouprocess=True, mean_var_path=None, supervised=False):
|
||||
self.n_states = n_states
|
||||
self.n_actions = n_actions
|
||||
self.alr = alr
|
||||
self.clr = clr
|
||||
self.model_name = model_name
|
||||
self.batch_size = batch_size
|
||||
self.gamma = gamma
|
||||
self.tau = tau
|
||||
self.ouprocess = ouprocess
|
||||
|
||||
if mean_var_path is None:
|
||||
mean = np.zeros(n_states)
|
||||
var = np.zeros(n_states)
|
||||
elif not os.path.exists(mean_var_path):
|
||||
mean = np.zeros(n_states)
|
||||
var = np.zeros(n_states)
|
||||
else:
|
||||
with open(mean_var_path, 'rb') as f:
|
||||
mean, var = pickle.load(f)
|
||||
|
||||
self.normalizer = Normalizer(mean, var)
|
||||
|
||||
if supervised:
|
||||
self._build_actor()
|
||||
LOG.info("Supervised Learning Initialized")
|
||||
else:
|
||||
# Build Network
|
||||
self._build_network()
|
||||
LOG.info('Finish Initializing Networks')
|
||||
|
||||
self.replay_memory = PrioritizedReplayMemory(capacity=memory_size)
|
||||
self.noise = OUProcess(n_actions)
|
||||
|
||||
@staticmethod
|
||||
def totensor(x):
|
||||
return Variable(torch.FloatTensor(x))
|
||||
|
||||
def _build_actor(self):
|
||||
if self.ouprocess:
|
||||
noisy = False
|
||||
else:
|
||||
noisy = True
|
||||
self.actor = Actor(self.n_states, self.n_actions, noisy=noisy)
|
||||
self.actor_criterion = nn.MSELoss()
|
||||
self.actor_optimizer = optimizer.Adam(lr=self.alr, params=self.actor.parameters())
|
||||
|
||||
def _build_network(self):
|
||||
if self.ouprocess:
|
||||
noisy = False
|
||||
else:
|
||||
noisy = True
|
||||
self.actor = Actor(self.n_states, self.n_actions, noisy=noisy)
|
||||
self.target_actor = Actor(self.n_states, self.n_actions)
|
||||
self.critic = Critic(self.n_states, self.n_actions)
|
||||
self.target_critic = Critic(self.n_states, self.n_actions)
|
||||
|
||||
# if model params are provided, load them
|
||||
if len(self.model_name):
|
||||
self.load_model(model_name=self.model_name)
|
||||
LOG.info("Loading model from file: %s", self.model_name)
|
||||
|
||||
# Copy actor's parameters
|
||||
self._update_target(self.target_actor, self.actor, tau=1.0)
|
||||
|
||||
# Copy critic's parameters
|
||||
self._update_target(self.target_critic, self.critic, tau=1.0)
|
||||
|
||||
self.loss_criterion = nn.MSELoss()
|
||||
self.actor_optimizer = optimizer.Adam(lr=self.alr, params=self.actor.parameters(),
|
||||
weight_decay=1e-5)
|
||||
self.critic_optimizer = optimizer.Adam(lr=self.clr, params=self.critic.parameters(),
|
||||
weight_decay=1e-5)
|
||||
|
||||
@staticmethod
|
||||
def _update_target(target, source, tau):
|
||||
for (target_param, param) in zip(target.parameters(), source.parameters()):
|
||||
target_param.data.copy_(
|
||||
target_param.data * (1 - tau) + param.data * tau
|
||||
)
|
||||
|
||||
def reset(self, sigma):
|
||||
self.noise.reset(sigma)
|
||||
|
||||
def _sample_batch(self):
|
||||
batch, idx = self.replay_memory.sample(self.batch_size)
|
||||
# batch = self.replay_memory.sample(self.batch_size)
|
||||
states = list(map(lambda x: x[0].tolist(), batch)) # pylint: disable=W0141
|
||||
next_states = list(map(lambda x: x[3].tolist(), batch)) # pylint: disable=W0141
|
||||
actions = list(map(lambda x: x[1].tolist(), batch)) # pylint: disable=W0141
|
||||
rewards = list(map(lambda x: x[2], batch)) # pylint: disable=W0141
|
||||
terminates = list(map(lambda x: x[4], batch)) # pylint: disable=W0141
|
||||
|
||||
return idx, states, next_states, actions, rewards, terminates
|
||||
|
||||
def add_sample(self, state, action, reward, next_state, terminate):
|
||||
self.critic.eval()
|
||||
self.actor.eval()
|
||||
self.target_critic.eval()
|
||||
self.target_actor.eval()
|
||||
batch_state = self.normalizer([state.tolist()])
|
||||
batch_next_state = self.normalizer([next_state.tolist()])
|
||||
current_value = self.critic(batch_state, self.totensor([action.tolist()]))
|
||||
target_action = self.target_actor(batch_next_state)
|
||||
target_value = self.totensor([reward]) \
|
||||
+ self.totensor([0 if x else 1 for x in [terminate]]) \
|
||||
* self.target_critic(batch_next_state, target_action) * self.gamma
|
||||
error = float(torch.abs(current_value - target_value).data.numpy()[0])
|
||||
|
||||
self.target_actor.train()
|
||||
self.actor.train()
|
||||
self.critic.train()
|
||||
self.target_critic.train()
|
||||
self.replay_memory.add(error, (state, action, reward, next_state, terminate))
|
||||
|
||||
def update(self):
|
||||
idxs, states, next_states, actions, rewards, terminates = self._sample_batch()
|
||||
batch_states = self.normalizer(states)
|
||||
batch_next_states = self.normalizer(next_states)
|
||||
batch_actions = self.totensor(actions)
|
||||
batch_rewards = self.totensor(rewards)
|
||||
mask = [0 if x else 1 for x in terminates]
|
||||
mask = self.totensor(mask)
|
||||
|
||||
target_next_actions = self.target_actor(batch_next_states).detach()
|
||||
target_next_value = self.target_critic(batch_next_states, target_next_actions).detach()
|
||||
current_value = self.critic(batch_states, batch_actions)
|
||||
# TODO (dongshen): This clause is the original clause, but it has some mistakes
|
||||
# next_value = batch_rewards + mask * target_next_value * self.gamma
|
||||
# Since terminate is always false, I remove the mask here.
|
||||
next_value = batch_rewards + target_next_value * self.gamma
|
||||
# Update Critic
|
||||
|
||||
# update prioritized memory
|
||||
error = torch.abs(current_value - next_value).data.numpy()
|
||||
for i in range(self.batch_size):
|
||||
idx = idxs[i]
|
||||
self.replay_memory.update(idx, error[i][0])
|
||||
|
||||
loss = self.loss_criterion(current_value, next_value)
|
||||
self.critic_optimizer.zero_grad()
|
||||
loss.backward()
|
||||
self.critic_optimizer.step()
|
||||
|
||||
# Update Actor
|
||||
self.critic.eval()
|
||||
policy_loss = -self.critic(batch_states, self.actor(batch_states))
|
||||
policy_loss = policy_loss.mean()
|
||||
self.actor_optimizer.zero_grad()
|
||||
policy_loss.backward()
|
||||
|
||||
self.actor_optimizer.step()
|
||||
self.critic.train()
|
||||
|
||||
self._update_target(self.target_critic, self.critic, tau=self.tau)
|
||||
self._update_target(self.target_actor, self.actor, tau=self.tau)
|
||||
|
||||
return loss.data, policy_loss.data
|
||||
|
||||
def choose_action(self, x):
|
||||
""" Select Action according to the current state
|
||||
Args:
|
||||
x: np.array, current state
|
||||
"""
|
||||
self.actor.eval()
|
||||
act = self.actor(self.normalizer([x.tolist()])).squeeze(0)
|
||||
self.actor.train()
|
||||
action = act.data.numpy()
|
||||
if self.ouprocess:
|
||||
action += self.noise.noise()
|
||||
return action.clip(0, 1)
|
||||
|
||||
def sample_noise(self):
|
||||
self.actor.sample_noise()
|
||||
|
||||
def load_model(self, model_name):
|
||||
""" Load Torch Model from files
|
||||
Args:
|
||||
model_name: str, model path
|
||||
"""
|
||||
self.actor.load_state_dict(
|
||||
torch.load('{}_actor.pth'.format(model_name))
|
||||
)
|
||||
self.critic.load_state_dict(
|
||||
torch.load('{}_critic.pth'.format(model_name))
|
||||
)
|
||||
|
||||
def save_model(self, model_name):
|
||||
""" Save Torch Model from files
|
||||
Args:
|
||||
model_dir: str, model dir
|
||||
title: str, model name
|
||||
"""
|
||||
torch.save(
|
||||
self.actor.state_dict(),
|
||||
'{}_actor.pth'.format(model_name)
|
||||
)
|
||||
|
||||
torch.save(
|
||||
self.critic.state_dict(),
|
||||
'{}_critic.pth'.format(model_name)
|
||||
)
|
||||
|
||||
def set_model(self, actor_dict, critic_dict):
|
||||
self.actor.load_state_dict(pickle.loads(actor_dict))
|
||||
self.critic.load_state_dict(pickle.loads(critic_dict))
|
||||
|
||||
def get_model(self):
|
||||
return pickle.dumps(self.actor.state_dict()), pickle.dumps(self.critic.state_dict())
|
||||
|
||||
def save_actor(self, path):
|
||||
""" save actor network
|
||||
Args:
|
||||
path, str, path to save
|
||||
"""
|
||||
torch.save(
|
||||
self.actor.state_dict(),
|
||||
path
|
||||
)
|
||||
|
||||
def load_actor(self, path):
|
||||
""" load actor network
|
||||
Args:
|
||||
path, str, path to load
|
||||
"""
|
||||
self.actor.load_state_dict(
|
||||
torch.load(path)
|
||||
)
|
||||
|
||||
def train_actor(self, batch_data, is_train=True):
|
||||
""" Train the actor separately with data
|
||||
Args:
|
||||
batch_data: tuple, (states, actions)
|
||||
is_train: bool
|
||||
Return:
|
||||
_loss: float, training loss
|
||||
"""
|
||||
states, action = batch_data
|
||||
|
||||
if is_train:
|
||||
self.actor.train()
|
||||
pred = self.actor(self.normalizer(states))
|
||||
action = self.totensor(action)
|
||||
|
||||
_loss = self.actor_criterion(pred, action)
|
||||
|
||||
self.actor_optimizer.zero_grad()
|
||||
_loss.backward()
|
||||
self.actor_optimizer.step()
|
||||
|
||||
else:
|
||||
self.actor.eval()
|
||||
pred = self.actor(self.normalizer(states))
|
||||
action = self.totensor(action)
|
||||
_loss = self.actor_criterion(pred, action)
|
||||
|
||||
return _loss.data[0]
|
||||
|
||||
@@ -1,41 +1,33 @@
|
||||
#
|
||||
# OUProcess.py
|
||||
#
|
||||
# Copyright
|
||||
#
|
||||
|
||||
import numpy as np
|
||||
|
||||
|
||||
# from https://github.com/songrotek/DDPG/blob/master/ou_noise.py
|
||||
class OUProcess(object):
|
||||
|
||||
def __init__(self, n_actions, theta=0.15, mu=0, sigma=0.1, ):
|
||||
|
||||
self.n_actions = n_actions
|
||||
self.theta = theta
|
||||
self.mu = mu
|
||||
self.sigma = sigma
|
||||
self.current_value = np.ones(self.n_actions) * self.mu
|
||||
|
||||
def reset(self, sigma=0):
|
||||
self.current_value = np.ones(self.n_actions) * self.mu
|
||||
if sigma != 0:
|
||||
self.sigma = sigma
|
||||
|
||||
def noise(self):
|
||||
x = self.current_value
|
||||
dx = self.theta * (self.mu - x) + self.sigma * np.random.randn(len(x))
|
||||
self.current_value = x + dx
|
||||
return self.current_value
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
import matplotlib.pyplot as plt # pylint: disable=wrong-import-position
|
||||
ou = OUProcess(3, theta=0.3) # pylint: disable=invalid-name
|
||||
states = [] # pylint: disable=invalid-name
|
||||
for i in range(1000):
|
||||
states.append(ou.noise())
|
||||
|
||||
plt.plot(states)
|
||||
plt.show()
|
||||
#
|
||||
# OtterTune - ou_process.py
|
||||
#
|
||||
# Copyright (c) 2017-18, Carnegie Mellon University Database Group
|
||||
#
|
||||
# from: https://github.com/KqSMea8/CDBTune
|
||||
# Zhang, Ji, et al. "An end-to-end automatic cloud database tuning system using
|
||||
# deep reinforcement learning." Proceedings of the 2019 International Conference
|
||||
# on Management of Data. ACM, 2019
|
||||
|
||||
import numpy as np
|
||||
|
||||
|
||||
class OUProcess(object):
|
||||
|
||||
def __init__(self, n_actions, theta=0.15, mu=0, sigma=0.1, ):
|
||||
|
||||
self.n_actions = n_actions
|
||||
self.theta = theta
|
||||
self.mu = mu
|
||||
self.sigma = sigma
|
||||
self.current_value = np.ones(self.n_actions) * self.mu
|
||||
|
||||
def reset(self, sigma=0):
|
||||
self.current_value = np.ones(self.n_actions) * self.mu
|
||||
if sigma != 0:
|
||||
self.sigma = sigma
|
||||
|
||||
def noise(self):
|
||||
x = self.current_value
|
||||
dx = self.theta * (self.mu - x) + self.sigma * np.random.randn(len(x))
|
||||
self.current_value = x + dx
|
||||
return self.current_value
|
||||
@@ -1,121 +1,132 @@
|
||||
#
|
||||
# prioritized_replay_memory.py
|
||||
#
|
||||
# Copyright
|
||||
#
|
||||
import random
|
||||
import pickle
|
||||
import numpy as np
|
||||
|
||||
|
||||
class SumTree(object):
|
||||
write = 0
|
||||
|
||||
def __init__(self, capacity):
|
||||
self.capacity = capacity
|
||||
self.tree = np.zeros(2 * capacity - 1)
|
||||
self.data = np.zeros(capacity, dtype=object)
|
||||
self.num_entries = 0
|
||||
|
||||
def _propagate(self, idx, change):
|
||||
parent = (idx - 1) // 2
|
||||
self.tree[parent] += change
|
||||
if parent != 0:
|
||||
self._propagate(parent, change)
|
||||
|
||||
def _retrieve(self, idx, s):
|
||||
left = 2 * idx + 1
|
||||
right = left + 1
|
||||
|
||||
if left >= len(self.tree):
|
||||
return idx
|
||||
|
||||
if s <= self.tree[left]:
|
||||
return self._retrieve(left, s)
|
||||
else:
|
||||
return self._retrieve(right, s - self.tree[left])
|
||||
|
||||
def total(self):
|
||||
return self.tree[0]
|
||||
|
||||
def add(self, p, data):
|
||||
idx = self.write + self.capacity - 1
|
||||
|
||||
self.data[self.write] = data
|
||||
self.update(idx, p)
|
||||
|
||||
self.write += 1
|
||||
if self.write >= self.capacity:
|
||||
self.write = 0
|
||||
if self.num_entries < self.capacity:
|
||||
self.num_entries += 1
|
||||
|
||||
def update(self, idx, p):
|
||||
change = p - self.tree[idx]
|
||||
|
||||
self.tree[idx] = p
|
||||
self._propagate(idx, change)
|
||||
|
||||
def get(self, s):
|
||||
idx = self._retrieve(0, s)
|
||||
data_idx = idx - self.capacity + 1
|
||||
return [idx, self.tree[idx], self.data[data_idx]]
|
||||
|
||||
|
||||
class PrioritizedReplayMemory(object):
|
||||
|
||||
def __init__(self, capacity):
|
||||
self.tree = SumTree(capacity)
|
||||
self.capacity = capacity
|
||||
self.e = 0.01 # pylint: disable=invalid-name
|
||||
self.a = 0.6 # pylint: disable=invalid-name
|
||||
self.beta = 0.4
|
||||
self.beta_increment_per_sampling = 0.001
|
||||
|
||||
def _get_priority(self, error):
|
||||
return (error + self.e) ** self.a
|
||||
|
||||
def add(self, error, sample):
|
||||
# (s, a, r, s, t)
|
||||
p = self._get_priority(error)
|
||||
self.tree.add(p, sample)
|
||||
|
||||
def __len__(self):
|
||||
return self.tree.num_entries
|
||||
|
||||
def sample(self, n):
|
||||
batch = []
|
||||
idxs = []
|
||||
segment = self.tree.total() / n
|
||||
priorities = []
|
||||
|
||||
self.beta = np.min([1., self.beta + self.beta_increment_per_sampling])
|
||||
|
||||
for i in range(n):
|
||||
a = segment * i
|
||||
b = segment * (i + 1)
|
||||
|
||||
s = random.uniform(a, b)
|
||||
(idx, p, data) = self.tree.get(s)
|
||||
priorities.append(p)
|
||||
batch.append(data)
|
||||
idxs.append(idx)
|
||||
return batch, idxs
|
||||
|
||||
# sampling_probabilities = priorities / self.tree.total()
|
||||
# is_weight = np.power(self.tree.num_entries * sampling_probabilities, -self.beta)
|
||||
# is_weight /= is_weight.max()
|
||||
|
||||
def update(self, idx, error):
|
||||
p = self._get_priority(error)
|
||||
self.tree.update(idx, p)
|
||||
|
||||
def save(self, path):
|
||||
f = open(path, 'wb')
|
||||
pickle.dump({"tree": self.tree}, f)
|
||||
f.close()
|
||||
|
||||
def load_memory(self, path):
|
||||
with open(path, 'rb') as f:
|
||||
_memory = pickle.load(f)
|
||||
self.tree = _memory['tree']
|
||||
#
|
||||
# OtterTune - prioritized_replay_memory.py
|
||||
#
|
||||
# Copyright (c) 2017-18, Carnegie Mellon University Database Group
|
||||
#
|
||||
# from: https://github.com/KqSMea8/CDBTune
|
||||
# Zhang, Ji, et al. "An end-to-end automatic cloud database tuning system using
|
||||
# deep reinforcement learning." Proceedings of the 2019 International Conference
|
||||
# on Management of Data. ACM, 2019
|
||||
|
||||
import random
|
||||
import pickle
|
||||
import numpy as np
|
||||
|
||||
|
||||
class SumTree(object):
|
||||
write = 0
|
||||
|
||||
def __init__(self, capacity):
|
||||
self.capacity = capacity
|
||||
self.tree = np.zeros(2 * capacity - 1)
|
||||
self.data = np.zeros(capacity, dtype=object)
|
||||
self.num_entries = 0
|
||||
|
||||
def _propagate(self, idx, change):
|
||||
parent = (idx - 1) // 2
|
||||
self.tree[parent] += change
|
||||
if parent != 0:
|
||||
self._propagate(parent, change)
|
||||
|
||||
def _retrieve(self, idx, s):
|
||||
left = 2 * idx + 1
|
||||
right = left + 1
|
||||
|
||||
if left >= len(self.tree):
|
||||
return idx
|
||||
|
||||
if s <= self.tree[left]:
|
||||
return self._retrieve(left, s)
|
||||
else:
|
||||
return self._retrieve(right, s - self.tree[left])
|
||||
|
||||
def total(self):
|
||||
return self.tree[0]
|
||||
|
||||
def add(self, p, data):
|
||||
idx = self.write + self.capacity - 1
|
||||
|
||||
self.data[self.write] = data
|
||||
self.update(idx, p)
|
||||
|
||||
self.write += 1
|
||||
if self.write >= self.capacity:
|
||||
self.write = 0
|
||||
if self.num_entries < self.capacity:
|
||||
self.num_entries += 1
|
||||
|
||||
def update(self, idx, p):
|
||||
change = p - self.tree[idx]
|
||||
|
||||
self.tree[idx] = p
|
||||
self._propagate(idx, change)
|
||||
|
||||
def get(self, s):
|
||||
idx = self._retrieve(0, s)
|
||||
data_idx = idx - self.capacity + 1
|
||||
return [idx, self.tree[idx], self.data[data_idx]]
|
||||
|
||||
|
||||
class PrioritizedReplayMemory(object):
|
||||
|
||||
def __init__(self, capacity):
|
||||
self.tree = SumTree(capacity)
|
||||
self.capacity = capacity
|
||||
self.e = 0.01 # pylint: disable=invalid-name
|
||||
self.a = 0.6 # pylint: disable=invalid-name
|
||||
self.beta = 0.4
|
||||
self.beta_increment_per_sampling = 0.001
|
||||
|
||||
def _get_priority(self, error):
|
||||
return (error + self.e) ** self.a
|
||||
|
||||
def add(self, error, sample):
|
||||
# (s, a, r, s, t)
|
||||
p = self._get_priority(error)
|
||||
self.tree.add(p, sample)
|
||||
|
||||
def __len__(self):
|
||||
return self.tree.num_entries
|
||||
|
||||
def sample(self, n):
|
||||
batch = []
|
||||
idxs = []
|
||||
segment = self.tree.total() / n
|
||||
priorities = []
|
||||
|
||||
self.beta = np.min([1., self.beta + self.beta_increment_per_sampling])
|
||||
|
||||
for i in range(n):
|
||||
a = segment * i
|
||||
b = segment * (i + 1)
|
||||
|
||||
s = random.uniform(a, b)
|
||||
(idx, p, data) = self.tree.get(s)
|
||||
priorities.append(p)
|
||||
batch.append(data)
|
||||
idxs.append(idx)
|
||||
return batch, idxs
|
||||
|
||||
# sampling_probabilities = priorities / self.tree.total()
|
||||
# is_weight = np.power(self.tree.num_entries * sampling_probabilities, -self.beta)
|
||||
# is_weight /= is_weight.max()
|
||||
|
||||
def update(self, idx, error):
|
||||
p = self._get_priority(error)
|
||||
self.tree.update(idx, p)
|
||||
|
||||
def save(self, path):
|
||||
f = open(path, 'wb')
|
||||
pickle.dump({"tree": self.tree}, f)
|
||||
f.close()
|
||||
|
||||
def load_memory(self, path):
|
||||
with open(path, 'rb') as f:
|
||||
_memory = pickle.load(f)
|
||||
self.tree = _memory['tree']
|
||||
|
||||
def get(self):
|
||||
return pickle.dumps({"tree": self.tree})
|
||||
|
||||
def set(self, binary):
|
||||
self.tree = pickle.loads(binary)['tree']
|
||||
|
||||
Reference in New Issue
Block a user