Add machine learning model ddpg

server/analysis/ddpg/OUProcess.py

@@ -0,0 +1,41 @@
+#
+# 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()

server/analysis/ddpg/__init__.py

@@ -0,0 +1,10 @@
+#
+# __init__.py
+#
+# Copyright
+#
+
+
+from analysis.ddpg.ddpg import DDPG
+
+__all__ = ["DDPG"]

server/analysis/ddpg/ddpg.py

@@ -0,0 +1,509 @@
+#
+# 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]

server/analysis/ddpg/prioritized_replay_memory.py

@@ -0,0 +1,121 @@
+#
+# 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']