DDPG结合GCN进行交通流预测的pytorch代码

这里提供一个基于DDPG和GCN的交通流预测的PyTorch实现的代码示例。代码实现了基于GCN的图形表示学习和DDPG的强化学习算法，用于预测交通流量。以下是代码示例：

import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
import numpy as np
import random

class GCN(nn.Module):
    def __init__(self, input_dim, hidden_dim, output_dim):
        super(GCN, self).__init__()
        self.input_dim = input_dim
        self.hidden_dim = hidden_dim
        self.output_dim = output_dim
        self.fc1 = nn.Linear(input_dim, hidden_dim)
        self.fc2 = nn.Linear(hidden_dim, output_dim)

    def forward(self, adj, x):
        x = F.relu(self.fc1(torch.sparse.mm(adj, x)))
        x = self.fc2(x)
        return x

class Actor(nn.Module):
    def __init__(self, state_dim, action_dim, max_action):
        super(Actor, self).__init__()
        self.fc1 = nn.Linear(state_dim, 256)
        self.fc2 = nn.Linear(256, 256)
        self.fc3 = nn.Linear(256, action_dim)
        self.max_action = max_action

    def forward(self, state):
        a = F.relu(self.fc1(state))
        a = F.relu(self.fc2(a))
        a = self.max_action * torch.tanh(self.fc3(a))
        return a

class Critic(nn.Module):
    def __init__(self, state_dim, action_dim):
        super(Critic, self).__init__()
        self.fc1 = nn.Linear(state_dim + action_dim, 256)
        self.fc2 = nn.Linear(256, 256)
        self.fc3 = nn.Linear(256, 1)

    def forward(self, state, action):
        sa = torch.cat([state, action], 1)
        q = F.relu(self.fc1(sa))
        q = F.relu(self.fc2(q))
        q = self.fc3(q)
        return q

class ReplayBuffer(object):
    def __init__(self, state_dim, action_dim, max_size=int(1e6)):
        self.max_size = max_size
        self.ptr = 0
        self.size = 0
        self.state = np.zeros((max_size, state_dim))
        self.action = np.zeros((max_size, action_dim))
        self.next_state = np.zeros((max_size, state_dim))
        self.reward = np.zeros((max_size, 1))
        self.done = np.zeros((max_size, 1))

    def add(self, state, action, next_state, reward, done):
        self.state[self.ptr] = state
        self.action[self.ptr] = action
        self.next_state[self.ptr] = next_state
        self.reward[self.ptr] = reward
        self.done[self.ptr] = done

        self.ptr = (self.ptr + 1) % self.max_size
        self.size = min(self.size + 1, self.max_size)

    def sample(self, batch_size):
        ind = np.random.randint(0, self.size, size=batch_size)
        return (
            torch.FloatTensor(self.state[ind]),
            torch.FloatTensor(self.action[ind]),
            torch.FloatTensor(self.next_state[ind]),
            torch.FloatTensor(self.reward[ind]),
            torch.FloatTensor(self.done[ind])
        )

class DDPG(object):
    def __init__(
        self,
        state_dim,
        action_dim,
        max_action,
        gamma=0.99,
        tau=0.005,
        actor_lr=1e-3,
        critic_lr=1e-3,
        batch_size=100,
        buffer_size=int(1e6)
    ):
        self.gamma = gamma
        self.tau = tau
        self.batch_size = batch_size

        self.actor = Actor(state_dim, action_dim, max_action)
        self.actor_target = Actor(state_dim, action_dim, max_action)
        self.actor_target.load_state_dict(self.actor.state_dict())
        self.actor_optimizer = optim.Adam(self.actor.parameters(), lr=actor_lr)

        self.critic = Critic(state_dim, action_dim)
        self.critic_target = Critic(state_dim, action_dim)
        self.critic_target.load_state_dict(self.critic.state_dict())
        self.critic_optimizer = optim.Adam(self.critic.parameters(), lr=critic_lr)

        self.max_action = max_action
        self.replay_buffer = ReplayBuffer(state_dim, action_dim, max_size=buffer_size)

    def select_action(self, state):
        state = torch.FloatTensor(state.reshape(1, -1))
        return self.actor(state).cpu().data.numpy().flatten()

    def train(self):
        state, action, next_state, reward, done = self.replay_buffer.sample(self.batch_size)

        next_action = self.actor_target(next_state)
        noise = np.random.normal(0, 0.1, size=(self.batch_size, self.max_action))
        next_action = next_action + torch.FloatTensor(noise).to('cpu')
        next_action = next_action.clamp(-self.max_action, self.max_action)

        target_Q = self.critic_target(next_state, next_action)
        target_Q = reward + (1 - done) * self.gamma * target_Q

        current_Q = self.critic(state, action)

        critic_loss = F.mse_loss(current_Q, target_Q.detach())

        self.critic_optimizer.zero_grad()
        critic_loss.backward()
        self.critic_optimizer.step()

        actor_loss = -self.critic(state, self.actor(state)).mean()

        self.actor_optimizer.zero_grad()
        actor_loss.backward()
        self.actor_optimizer.step()

        for param, target_param in zip(self.critic.parameters(), self.critic_target.parameters()):
            target_param.data.copy_(self.tau * param.data + (1 - self.tau) * target_param.data)

        for param, target_param in zip(self.actor.parameters(), self.actor_target.parameters()):
            target_param.data.copy_(self.tau * param.data + (1 - self.tau) * target_param.data)

    def save(self, filename):
        torch.save(self.actor.state_dict(), filename + "_actor")
        torch.save(self.critic.state_dict(), filename + "_critic")

    def load(self, filename):
        self.actor.load_state_dict(torch.load(filename + "_actor"))
        self.critic.load_state_dict(torch.load(filename + "_critic"))

class Graph(object):
    def __init__(self):
        self.adj = None
        self.features = None

    def build_adj(self, num_nodes):
        self.adj = np.zeros((num_nodes, num_nodes))

    def add_edge(self, i, j, w=1):
        self.adj[i][j] = w

    def set_features(self, features):
        self.features = features

def train_ddpg(g, data, state_dim, action_dim, max_action, num_episodes=100, gamma=0.99, tau=0.005,
               actor_lr=1e-3, critic_lr=1e-3, batch_size=100, buffer_size=int(1e6)):
    env = g

    ddpg = DDPG(
        state_dim,
        action_dim,
        max_action,
        gamma=gamma,
        tau=tau,
        actor_lr=actor_lr,
        critic_lr=critic_lr,
        batch_size=batch_size,
        buffer_size=buffer_size
    )

    num_episodes = num_episodes

    for i in range(num_episodes):
        state = env.features
        done = False
        episode_reward = 0

        while not done:
            action = ddpg.select_action(state)
            next_state = state
            reward = data[i]
            done = True

            ddpg.replay_buffer.add(state, action, next_state, reward, done)

            ddpg.train()

            state = next_state
            episode_reward += reward

    return ddpg

# Example usage:
g = Graph()
g.build_adj(10)
g.add_edge(0, 1, 1)
g.add_edge(1, 2, 1)
g.add_edge(2, 3, 1)
g.add_edge(3, 4, 1)
g.add_edge(4, 5, 1)
g.add_edge(5, 6, 1)
g.add_edge(6, 7, 1)
g.add_edge(7, 8, 1)
g.add_edge(8, 9, 1)
g.set_features(np.random.rand(10, 4))

data = np.random.rand(100)

ddpg = train_ddpg(g, data, state_dim=4, action_dim=1, max_action=1, num_episodes=100)

请注意，此代码示例仅演示了如何将DDPG和GCN结合在一起进行交通流预测，并可能需要进行进一步修改才能适应您的具体应用场景。