simplify ddpg
This commit is contained in:
yangdsh
Dana Van Aken
parent
336221d886
commit
21f4f40b88
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@ -8,14 +8,9 @@
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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 os
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import pickle
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import math
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import numpy as np
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import torch
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import torch.nn as nn
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from torch.nn import init, Parameter
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import torch.nn.functional as F
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import torch.optim as optimizer
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from torch.autograd import Variable
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@ -26,68 +21,9 @@ from analysis.util import get_analysis_logger
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LOG = get_analysis_logger(__name__)
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# code from https://github.com/Kaixhin/NoisyNet-A3C/blob/master/model.py
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class NoisyLinear(nn.Linear):
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def __init__(self, in_features, out_features, sigma_init=0.05, bias=True):
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super(NoisyLinear, self).__init__(in_features, out_features, bias=True)
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# reuse self.weight and self.bias
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self.sigma_init = sigma_init
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self.sigma_weight = Parameter(torch.Tensor(out_features, in_features))
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self.sigma_bias = Parameter(torch.Tensor(out_features))
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self.epsilon_weight = None
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self.epsilon_bias = None
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self.register_buffer('epsilon_weight', torch.zeros(out_features, in_features))
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self.register_buffer('epsilon_bias', torch.zeros(out_features))
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self.reset_parameters()
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def reset_parameters(self):
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# Only init after all params added (otherwise super().__init__() fails)
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if hasattr(self, 'sigma_weight'):
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init.uniform(self.weight, -math.sqrt(3 / self.in_features),
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math.sqrt(3 / self.in_features))
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init.uniform(self.bias, -math.sqrt(3 / self.in_features),
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math.sqrt(3 / self.in_features))
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init.constant(self.sigma_weight, self.sigma_init)
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init.constant(self.sigma_bias, self.sigma_init)
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def forward(self, x):
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return F.linear(x, self.weight + self.sigma_weight * Variable(self.epsilon_weight),
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self.bias + self.sigma_bias * Variable(self.epsilon_bias))
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def sample_noise(self):
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self.epsilon_weight = torch.randn(self.out_features, self.in_features)
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self.epsilon_bias = torch.randn(self.out_features)
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def remove_noise(self):
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self.epsilon_weight = torch.zeros(self.out_features, self.in_features)
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self.epsilon_bias = torch.zeros(self.out_features)
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class Normalizer(object):
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def __init__(self, mean, variance):
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if isinstance(mean, list):
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mean = np.array(mean)
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if isinstance(variance, list):
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variance = np.array(variance)
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self.mean = mean
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self.std = np.sqrt(variance + 0.00001)
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def normalize(self, x):
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if isinstance(x, list):
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x = np.array(x)
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x = x - self.mean
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x = x / self.std
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return Variable(torch.FloatTensor(x))
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def __call__(self, x, *args, **kwargs):
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return self.normalize(x)
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class Actor(nn.Module):
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def __init__(self, n_states, n_actions, noisy=False):
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def __init__(self, n_states, n_actions):
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super(Actor, self).__init__()
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self.layers = nn.Sequential(
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nn.Linear(n_states, 128),
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@ -100,13 +36,11 @@ class Actor(nn.Module):
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nn.Linear(128, 64),
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nn.Tanh(),
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nn.BatchNorm1d(64),
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nn.Linear(64, n_actions)
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)
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if noisy:
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self.out = NoisyLinear(64, n_actions)
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else:
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self.out = nn.Linear(64, n_actions)
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self._init_weights()
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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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@ -115,13 +49,10 @@ class Actor(nn.Module):
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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 sample_noise(self):
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self.out.sample_noise()
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def forward(self, states): # pylint: disable=arguments-differ
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def forward(self, x): # pylint: disable=arguments-differ
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out = self.act(self.out(self.layers(x)))
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return out
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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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@ -156,11 +87,11 @@ class Critic(nn.Module):
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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, x, action): # pylint: disable=arguments-differ
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x = self.act(self.state_input(x))
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action = self.act(self.action_input(action))
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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([x, action], dim=1)
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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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@ -168,8 +99,7 @@ class Critic(nn.Module):
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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, memory_size=100000,
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ouprocess=True, mean_var_path=None, supervised=False):
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gamma=0.9, batch_size=32, tau=0.002, memory_size=100000):
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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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@ -178,25 +108,7 @@ class DDPG(object):
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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.ouprocess = ouprocess
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if mean_var_path is None:
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mean = np.zeros(n_states)
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var = np.zeros(n_states)
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elif not os.path.exists(mean_var_path):
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mean = np.zeros(n_states)
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var = np.zeros(n_states)
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else:
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with open(mean_var_path, 'rb') as f:
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mean, var = pickle.load(f)
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self.normalizer = Normalizer(mean, var)
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if supervised:
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self._build_actor()
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LOG.info("Supervised Learning Initialized")
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else:
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# Build Network
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self._build_network()
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self.replay_memory = PrioritizedReplayMemory(capacity=memory_size)
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@ -206,30 +118,12 @@ class DDPG(object):
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def totensor(x):
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return Variable(torch.FloatTensor(x))
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def _build_actor(self):
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if self.ouprocess:
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noisy = False
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else:
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noisy = True
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self.actor = Actor(self.n_states, self.n_actions, noisy=noisy)
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self.actor_criterion = nn.MSELoss()
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self.actor_optimizer = optimizer.Adam(lr=self.alr, params=self.actor.parameters())
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def _build_network(self):
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if self.ouprocess:
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noisy = False
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else:
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noisy = True
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self.actor = Actor(self.n_states, self.n_actions, noisy=noisy)
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self.actor = Actor(self.n_states, self.n_actions)
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self.target_actor = Actor(self.n_states, self.n_actions)
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self.critic = Critic(self.n_states, self.n_actions)
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self.target_critic = Critic(self.n_states, self.n_actions)
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# if model params are provided, load them
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if len(self.model_name):
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self.load_model(model_name=self.model_name)
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LOG.info("Loading model from file: %s", self.model_name)
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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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@ -249,64 +143,57 @@ class DDPG(object):
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target_param.data * (1 - tau) + param.data * tau
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)
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def reset(self, sigma):
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self.noise.reset(sigma)
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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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# batch = 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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next_states = list(map(lambda x: x[3].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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terminates = list(map(lambda x: x[4], 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, terminates
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return idx, states, next_states, actions, rewards
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def add_sample(self, state, action, reward, next_state, terminate):
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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.normalizer([state.tolist()])
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batch_next_state = self.normalizer([next_state.tolist()])
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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.totensor([0 if x else 1 for x in [terminate]]) \
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* self.target_critic(batch_next_state, target_action) * self.gamma
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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, terminate))
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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, terminates = self._sample_batch()
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batch_states = self.normalizer(states)
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batch_next_states = self.normalizer(next_states)
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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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mask = [0 if x else 1 for x in terminates]
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mask = self.totensor(mask)
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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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# TODO (dongshen): This clause is the original clause, but it has some mistakes
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# next_value = batch_rewards + mask * target_next_value * self.gamma
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# Since terminate is always false, I remove the mask here.
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next_value = batch_rewards + target_next_value * self.gamma
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# Update Critic
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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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@ -327,101 +214,17 @@ class DDPG(object):
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return loss.data, policy_loss.data
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def choose_action(self, x):
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""" Select Action according to the current state
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Args:
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x: np.array, current state
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"""
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def choose_action(self, states):
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self.actor.eval()
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act = self.actor(self.normalizer([x.tolist()])).squeeze(0)
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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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if self.ouprocess:
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action += self.noise.noise()
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return action.clip(0, 1)
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def sample_noise(self):
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self.actor.sample_noise()
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def load_model(self, model_name):
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""" Load Torch Model from files
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Args:
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model_name: str, model path
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"""
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self.actor.load_state_dict(
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torch.load('{}_actor.pth'.format(model_name))
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)
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self.critic.load_state_dict(
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torch.load('{}_critic.pth'.format(model_name))
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)
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def save_model(self, model_name):
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""" Save Torch Model from files
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Args:
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model_dir: str, model dir
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title: str, model name
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"""
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torch.save(
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self.actor.state_dict(),
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'{}_actor.pth'.format(model_name)
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)
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torch.save(
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self.critic.state_dict(),
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'{}_critic.pth'.format(model_name)
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)
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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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def save_actor(self, path):
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""" save actor network
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Args:
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path, str, path to save
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"""
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torch.save(
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self.actor.state_dict(),
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path
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)
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def load_actor(self, path):
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""" load actor network
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Args:
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path, str, path to load
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"""
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self.actor.load_state_dict(
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torch.load(path)
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)
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def train_actor(self, batch_data, is_train=True):
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""" Train the actor separately with data
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Args:
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batch_data: tuple, (states, actions)
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is_train: bool
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Return:
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_loss: float, training loss
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"""
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states, action = batch_data
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if is_train:
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self.actor.train()
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pred = self.actor(self.normalizer(states))
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action = self.totensor(action)
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_loss = self.actor_criterion(pred, action)
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self.actor_optimizer.zero_grad()
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_loss.backward()
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self.actor_optimizer.step()
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else:
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self.actor.eval()
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pred = self.actor(self.normalizer(states))
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action = self.totensor(action)
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_loss = self.actor_criterion(pred, action)
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return _loss.data[0]
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@ -21,10 +21,12 @@ class OUProcess(object):
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self.sigma = sigma
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self.current_value = np.ones(self.n_actions) * self.mu
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def reset(self, sigma=0):
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def reset(self, sigma=0, theta=0):
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self.current_value = np.ones(self.n_actions) * self.mu
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if sigma != 0:
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self.sigma = sigma
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if theta != 0:
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self.theta = theta
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def noise(self):
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x = self.current_value
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@ -32,8 +32,7 @@ class TestDDPG(unittest.TestCase):
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metric_data = np.array([random.random()])
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reward = 1.0 if (prev_metric_data[0] - 0.5) * (knob_data[0] - 0.5) > 0 else 0.0
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reward = np.array([reward])
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cls.ddpg.add_sample(prev_metric_data, knob_data, reward, metric_data, False)
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if len(cls.ddpg.replay_memory) > 32:
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cls.ddpg.add_sample(prev_metric_data, knob_data, reward, metric_data)
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cls.ddpg.update()
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def test_ddpg_ypreds(self):
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@ -325,20 +325,19 @@ def train_ddpg(result_id):
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# Calculate the reward
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objective = objective / base_objective
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if metric_meta[target_objective].improvement == '(less is better)':
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reward = -objective * objective
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reward = -objective
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else:
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reward = objective * objective
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reward = objective
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LOG.info('reward: %f', reward)
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# Update ddpg
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ddpg = DDPG(n_actions=knob_num, n_states=metric_num, alr=ACTOR_LEARNING_RATE,
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clr=CRITIC_LEARNING_RATE, gamma=0.0, batch_size=DDPG_BATCH_SIZE, tau=0.0)
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clr=CRITIC_LEARNING_RATE, gamma=0, batch_size=DDPG_BATCH_SIZE)
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if session.ddpg_actor_model and session.ddpg_critic_model:
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ddpg.set_model(session.ddpg_actor_model, session.ddpg_critic_model)
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if session.ddpg_reply_memory:
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ddpg.replay_memory.set(session.ddpg_reply_memory)
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ddpg.add_sample(normalized_metric_data, knob_data, reward, normalized_metric_data, False)
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if len(ddpg.replay_memory) > 32:
|
||||
ddpg.add_sample(normalized_metric_data, knob_data, reward, normalized_metric_data)
|
||||
ddpg.update()
|
||||
session.ddpg_actor_model, session.ddpg_critic_model = ddpg.get_model()
|
||||
session.ddpg_reply_memory = ddpg.replay_memory.get()
|
||||
|
|
@ -362,8 +361,7 @@ def configuration_recommendation_ddpg(result_info): # pylint: disable=invalid-n
|
|||
knob_num = len(knob_labels)
|
||||
metric_num = len(metric_data)
|
||||
|
||||
ddpg = DDPG(n_actions=knob_num, n_states=metric_num, alr=ACTOR_LEARNING_RATE,
|
||||
clr=CRITIC_LEARNING_RATE, gamma=0.0, batch_size=DDPG_BATCH_SIZE, tau=0.0)
|
||||
ddpg = DDPG(n_actions=knob_num, n_states=metric_num)
|
||||
if session.ddpg_actor_model is not None and session.ddpg_critic_model is not None:
|
||||
ddpg.set_model(session.ddpg_actor_model, session.ddpg_critic_model)
|
||||
if session.ddpg_reply_memory is not None:
|
||||
|
|
|
|||
Loading…
Reference in New Issue