ottertune/server/analysis/ddpg/ddpg.py

233 lines
8.1 KiB
Python

#
# 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 pickle
import torch
import torch.nn as nn
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__)
class Actor(nn.Module):
def __init__(self, n_states, n_actions):
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.BatchNorm1d(128),
nn.Linear(128, 64),
nn.Tanh(),
nn.BatchNorm1d(64),
nn.Linear(64, n_actions)
)
# This act layer maps the output to (0, 1)
self.act = nn.Sigmoid()
self._init_weights()
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 forward(self, states): # pylint: disable=arguments-differ
actions = self.act(self.layers(states))
return actions
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, states, actions): # pylint: disable=arguments-differ
states = self.act(self.state_input(states))
actions = self.act(self.action_input(actions))
_input = torch.cat([states, actions], 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, shift=0, memory_size=100000):
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.shift = shift
self._build_network()
self.replay_memory = PrioritizedReplayMemory(capacity=memory_size)
self.noise = OUProcess(n_actions)
@staticmethod
def totensor(x):
return Variable(torch.FloatTensor(x))
def _build_network(self):
self.actor = Actor(self.n_states, self.n_actions)
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)
# 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, theta):
self.noise.reset(sigma, theta)
def _sample_batch(self):
batch, idx = self.replay_memory.sample(self.batch_size)
states = list(map(lambda x: x[0].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
next_states = list(map(lambda x: x[3].tolist(), batch)) # pylint: disable=W0141
return idx, states, next_states, actions, rewards
def add_sample(self, state, action, reward, next_state):
self.critic.eval()
self.actor.eval()
self.target_critic.eval()
self.target_actor.eval()
batch_state = self.totensor([state.tolist()])
batch_next_state = self.totensor([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.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))
def update(self):
idxs, states, next_states, actions, rewards = self._sample_batch()
batch_states = self.totensor(states)
batch_next_states = self.totensor(next_states)
batch_actions = self.totensor(actions)
batch_rewards = self.totensor(rewards)
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)
next_value = batch_rewards + target_next_value * self.gamma + self.shift
# update prioritized memory
if isinstance(self.replay_memory, PrioritizedReplayMemory):
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])
# Update Critic
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, states):
self.actor.eval()
act = self.actor(self.totensor([states.tolist()])).squeeze(0)
self.actor.train()
action = act.data.numpy()
action += self.noise.noise()
return action.clip(0, 1)
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())