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# 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()
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