236 lines
8.7 KiB
Python
236 lines
8.7 KiB
Python
#
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# OtterTune - ddpg.py
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#
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# Copyright (c) 2017-18, Carnegie Mellon University Database Group
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#
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# from: https://github.com/KqSMea8/CDBTune
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# Zhang, Ji, et al. "An end-to-end automatic cloud database tuning system using
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# deep reinforcement learning." Proceedings of the 2019 International Conference
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# on Management of Data. ACM, 2019
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import pickle
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import torch
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import torch.nn as nn
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import torch.optim as optimizer
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from torch.autograd import Variable
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from analysis.ddpg.ou_process import OUProcess
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from analysis.ddpg.prioritized_replay_memory import PrioritizedReplayMemory
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from analysis.util import get_analysis_logger
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LOG = get_analysis_logger(__name__)
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class Actor(nn.Module):
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def __init__(self, n_states, n_actions, hidden_sizes):
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super(Actor, self).__init__()
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self.layers = nn.Sequential(
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nn.Linear(n_states, hidden_sizes[0]),
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nn.LeakyReLU(negative_slope=0.2),
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nn.BatchNorm1d(hidden_sizes[0]),
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nn.Linear(hidden_sizes[0], hidden_sizes[1]),
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nn.Tanh(),
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nn.Dropout(0.3),
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nn.BatchNorm1d(hidden_sizes[1]),
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nn.Linear(hidden_sizes[1], hidden_sizes[2]),
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nn.Tanh(),
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nn.Dropout(0.3),
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nn.BatchNorm1d(hidden_sizes[2]),
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nn.Linear(hidden_sizes[2], n_actions)
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)
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# This act layer maps the output to (0, 1)
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self.act = nn.Sigmoid()
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self._init_weights()
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def _init_weights(self):
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for m in self.layers:
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if isinstance(m, nn.Linear):
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m.weight.data.normal_(0.0, 1e-2)
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m.bias.data.uniform_(-0.1, 0.1)
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def forward(self, states): # pylint: disable=arguments-differ
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actions = self.act(self.layers(states))
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return actions
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class Critic(nn.Module):
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def __init__(self, n_states, n_actions, hidden_sizes):
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super(Critic, self).__init__()
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self.state_input = nn.Linear(n_states, hidden_sizes[0])
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self.action_input = nn.Linear(n_actions, hidden_sizes[0])
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self.act = nn.Tanh()
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self.layers = nn.Sequential(
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nn.Linear(hidden_sizes[0] * 2, hidden_sizes[1]),
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nn.LeakyReLU(negative_slope=0.2),
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nn.Dropout(0.3),
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nn.BatchNorm1d(hidden_sizes[1]),
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nn.Linear(hidden_sizes[1], hidden_sizes[2]),
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nn.Tanh(),
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nn.Dropout(0.3),
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nn.BatchNorm1d(hidden_sizes[2]),
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nn.Linear(hidden_sizes[2], 1),
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)
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self._init_weights()
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def _init_weights(self):
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self.state_input.weight.data.normal_(0.0, 1e-2)
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self.state_input.bias.data.uniform_(-0.1, 0.1)
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self.action_input.weight.data.normal_(0.0, 1e-2)
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self.action_input.bias.data.uniform_(-0.1, 0.1)
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for m in self.layers:
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if isinstance(m, nn.Linear):
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m.weight.data.normal_(0.0, 1e-2)
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m.bias.data.uniform_(-0.1, 0.1)
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def forward(self, states, actions): # pylint: disable=arguments-differ
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states = self.act(self.state_input(states))
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actions = self.act(self.action_input(actions))
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_input = torch.cat([states, actions], dim=1)
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value = self.layers(_input)
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return value
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class DDPG(object):
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def __init__(self, n_states, n_actions, model_name='', alr=0.001, clr=0.001,
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gamma=0.9, batch_size=32, tau=0.002, shift=0, memory_size=100000,
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a_hidden_sizes=[128, 128, 64], c_hidden_sizes=[128, 256, 64]):
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self.n_states = n_states
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self.n_actions = n_actions
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self.alr = alr
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self.clr = clr
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self.model_name = model_name
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self.batch_size = batch_size
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self.gamma = gamma
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self.tau = tau
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self.a_hidden_sizes = a_hidden_sizes
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self.c_hidden_sizes = c_hidden_sizes
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self.shift = shift
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self._build_network()
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self.replay_memory = PrioritizedReplayMemory(capacity=memory_size)
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self.noise = OUProcess(n_actions)
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@staticmethod
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def totensor(x):
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return Variable(torch.FloatTensor(x))
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def _build_network(self):
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self.actor = Actor(self.n_states, self.n_actions, self.a_hidden_sizes)
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self.target_actor = Actor(self.n_states, self.n_actions, self.a_hidden_sizes)
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self.critic = Critic(self.n_states, self.n_actions, self.c_hidden_sizes)
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self.target_critic = Critic(self.n_states, self.n_actions, self.c_hidden_sizes)
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# Copy actor's parameters
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self._update_target(self.target_actor, self.actor, tau=1.0)
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# Copy critic's parameters
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self._update_target(self.target_critic, self.critic, tau=1.0)
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self.loss_criterion = nn.MSELoss()
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self.actor_optimizer = optimizer.Adam(lr=self.alr, params=self.actor.parameters(),
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weight_decay=1e-5)
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self.critic_optimizer = optimizer.Adam(lr=self.clr, params=self.critic.parameters(),
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weight_decay=1e-5)
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@staticmethod
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def _update_target(target, source, tau):
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for (target_param, param) in zip(target.parameters(), source.parameters()):
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target_param.data.copy_(
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target_param.data * (1 - tau) + param.data * tau
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)
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def reset(self, sigma, theta):
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self.noise.reset(sigma, theta)
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def _sample_batch(self):
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batch, idx = self.replay_memory.sample(self.batch_size)
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states = list(map(lambda x: x[0].tolist(), batch)) # pylint: disable=W0141
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actions = list(map(lambda x: x[1].tolist(), batch)) # pylint: disable=W0141
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rewards = list(map(lambda x: x[2], batch)) # pylint: disable=W0141
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next_states = list(map(lambda x: x[3].tolist(), batch)) # pylint: disable=W0141
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return idx, states, next_states, actions, rewards
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def add_sample(self, state, action, reward, next_state):
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self.critic.eval()
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self.actor.eval()
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self.target_critic.eval()
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self.target_actor.eval()
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batch_state = self.totensor([state.tolist()])
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batch_next_state = self.totensor([next_state.tolist()])
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current_value = self.critic(batch_state, self.totensor([action.tolist()]))
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target_action = self.target_actor(batch_next_state)
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target_value = self.totensor([reward]) \
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+ self.target_critic(batch_next_state, target_action) * self.gamma
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error = float(torch.abs(current_value - target_value).data.numpy()[0])
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self.target_actor.train()
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self.actor.train()
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self.critic.train()
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self.target_critic.train()
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self.replay_memory.add(error, (state, action, reward, next_state))
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def update(self):
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idxs, states, next_states, actions, rewards = self._sample_batch()
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batch_states = self.totensor(states)
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batch_next_states = self.totensor(next_states)
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batch_actions = self.totensor(actions)
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batch_rewards = self.totensor(rewards)
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target_next_actions = self.target_actor(batch_next_states).detach()
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target_next_value = self.target_critic(batch_next_states, target_next_actions).detach()
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current_value = self.critic(batch_states, batch_actions)
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next_value = batch_rewards + target_next_value * self.gamma + self.shift
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# update prioritized memory
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if isinstance(self.replay_memory, PrioritizedReplayMemory):
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error = torch.abs(current_value - next_value).data.numpy()
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for i in range(self.batch_size):
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idx = idxs[i]
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self.replay_memory.update(idx, error[i][0])
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# Update Critic
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loss = self.loss_criterion(current_value, next_value)
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self.critic_optimizer.zero_grad()
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loss.backward()
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self.critic_optimizer.step()
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# Update Actor
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self.critic.eval()
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policy_loss = -self.critic(batch_states, self.actor(batch_states))
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policy_loss = policy_loss.mean()
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self.actor_optimizer.zero_grad()
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policy_loss.backward()
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self.actor_optimizer.step()
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self.critic.train()
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self._update_target(self.target_critic, self.critic, tau=self.tau)
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self._update_target(self.target_actor, self.actor, tau=self.tau)
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return loss.data, policy_loss.data
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def choose_action(self, states):
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self.actor.eval()
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act = self.actor(self.totensor([states.tolist()])).squeeze(0)
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self.actor.train()
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action = act.data.numpy()
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action += self.noise.noise()
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return action.clip(0, 1)
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def set_model(self, actor_dict, critic_dict):
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self.actor.load_state_dict(pickle.loads(actor_dict))
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self.critic.load_state_dict(pickle.loads(critic_dict))
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def get_model(self):
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return pickle.dumps(self.actor.state_dict()), pickle.dumps(self.critic.state_dict())
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