AI Customer Behavior Agent/backend/src/dqn_agent.py

# File: src/dqn_agent.py
import torch
import torch.nn as nn
import torch.optim as optim
import random
import numpy as np
from collections import deque

# Dueling DQN network architecture for state‑action value estimation
class DuelingDQN(nn.Module):
    def __init__(self, state_size, action_size):
        super(DuelingDQN, self).__init__()
        self.fc1 = nn.Linear(state_size, 128)
        self.fc2 = nn.Linear(128, 128)
        # Value stream
        self.value_stream = nn.Sequential(
            nn.Linear(128, 64),
            nn.ReLU(),
            nn.Linear(64, 1)
        )
        # Advantage stream
        self.advantage_stream = nn.Sequential(
            nn.Linear(128, 64),
            nn.ReLU(),
            nn.Linear(64, action_size)
        )
    
    def forward(self, x):
        x = torch.relu(self.fc1(x))
        x = torch.relu(self.fc2(x))
        value = self.value_stream(x)
        advantage = self.advantage_stream(x)
        # Combine streams to get Q-values
        q_values = value + (advantage - advantage.mean(dim=1, keepdim=True))
        return q_values

class AdvancedDQNAgent:
    def __init__(self, state_size, action_size, device="cpu"):
        self.state_size = state_size
        self.action_size = action_size
        self.device = device
        
        self.memory = deque(maxlen=10000)
        self.gamma = 0.99  # discount factor
        self.epsilon = 1.0  # exploration rate
        self.epsilon_min = 0.01
        self.epsilon_decay = 0.995
        self.learning_rate = 0.001
        self.batch_size = 64
        
        self.policy_net = DuelingDQN(state_size, action_size).to(device)
        self.target_net = DuelingDQN(state_size, action_size).to(device)
        self.update_target_network()
        self.optimizer = optim.Adam(self.policy_net.parameters(), lr=self.learning_rate)
        self.criterion = nn.MSELoss()

    def update_target_network(self):
        self.target_net.load_state_dict(self.policy_net.state_dict())

    def act(self, state):
        if np.random.rand() <= self.epsilon:
            return random.randrange(self.action_size)
        state_tensor = torch.FloatTensor(state).unsqueeze(0).to(self.device)
        with torch.no_grad():
            q_values = self.policy_net(state_tensor)
        return int(torch.argmax(q_values).item())
    
    def remember(self, state, action, reward, next_state, done):
        self.memory.append((state, action, reward, next_state, done))
    
    def replay(self):
        if len(self.memory) < self.batch_size:
            return
        
        batch = random.sample(self.memory, self.batch_size)
        states, actions, rewards, next_states, dones = zip(*batch)
        states = torch.FloatTensor(states).to(self.device)
        actions = torch.LongTensor(actions).unsqueeze(1).to(self.device)
        rewards = torch.FloatTensor(rewards).unsqueeze(1).to(self.device)
        next_states = torch.FloatTensor(next_states).to(self.device)
        dones = torch.FloatTensor(dones).unsqueeze(1).to(self.device)
        
        # Compute current Q-values
        current_q = self.policy_net(states).gather(1, actions)
        # Double DQN: select next action using policy net, evaluate with target net
        next_actions = torch.argmax(self.policy_net(next_states), dim=1, keepdim=True)
        next_q = self.target_net(next_states).gather(1, next_actions)
        target_q = rewards + (self.gamma * next_q * (1 - dones))
        
        loss = self.criterion(current_q, target_q.detach())
        self.optimizer.zero_grad()
        loss.backward()
        self.optimizer.step()
        
        if self.epsilon > self.epsilon_min:
            self.epsilon *= self.epsilon_decay
            
    def save(self, path):
        torch.save(self.policy_net.state_dict(), path)
    
    def load(self, path):
        self.policy_net.load_state_dict(torch.load(path))
        self.update_target_network()