dqn python

DQN (Deep Q-Network) is a popular reinforcement learning algorithm used for training agents to make decisions in environments with discrete action spaces. In Python, you can implement DQN using popular deep learning libraries such as TensorFlow or PyTorch.

Here's a simple example of how to implement DQN in Python using the PyTorch library:

1. Install the required libraries:

pip install gym torch torchvision numpy

2. Import the necessary libraries:

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

3. Define the Q-network:

class QNetwork(nn.Module):
    def __init__(self, state_size, action_size):
        super(QNetwork, self).__init__()
        self.fc1 = nn.Linear(state_size, 64)
        self.fc2 = nn.Linear(64, 64)
        self.fc3 = nn.Linear(64, action_size)

    def forward(self, x):
        x = F.relu(self.fc1(x))
        x = F.relu(self.fc2(x))
        x = self.fc3(x)
        return x

4. Initialize the environment and hyperparameters:

env = gym.make("CartPole-v0")
state_size = env.observation_space.shape[0]
action_size = env.action_space.n
batch_size = 32
gamma = 0.99
epsilon = 1.0
epsilon_decay = 0.995
epsilon_min = 0.01
memory = []
model = QNetwork(state_size, action_size)
optimizer = optim.Adam(model.parameters(), lr=0.001)

5. Define the replay memory and epsilon-greedy exploration:

def remember(state, action, reward, next_state, done):
    memory.append((state, action, reward, next_state, done))

def choose_action(state):
    if np.random.rand() <= epsilon:
        return env.action_space.sample()
    else:
        state = torch.tensor(state, dtype=torch.float32).unsqueeze(0)
        q_values = model(state)
        return torch.argmax(q_values).item()

6. Define the training loop:

def replay_experience():
    if len(memory) < batch_size:
        return

    batch = np.random.choice(len(memory), batch_size, replace=False)
    states, actions, rewards, next_states, dones = zip(*[memory[i] for i in batch])

    states = torch.tensor(states, dtype=torch.float32)
    actions = torch.tensor(actions, dtype=torch.long)
    rewards = torch.tensor(rewards, dtype=torch.float32)
    next_states = torch.tensor(next_states, dtype=torch.float32)
    dones = torch.tensor(dones, dtype=torch.float32)

    q_values = model(states)
    next_q_values = model(next_states)

    q_values = q_values.gather(1, actions.unsqueeze(1)).squeeze(1)
    next_q_values = next_q_values.max(1)[0]
    expected_q_values = rewards + gamma * next_q_values * (1 - dones)

    loss = F.smooth_l1_loss(q_values, expected_q_values)
    optimizer.zero_grad()
    loss.backward()
    optimizer.step()

def train_agent(num_episodes):
    global epsilon

    for episode in range(num_episodes):
        state = env.reset()
        total_reward = 0

        while True:
            action = choose_action(state)
            next_state, reward, done, _ = env.step(action)
            remember(state, action, reward, next_state, done)
            total_reward += reward
            state = next_state

            replay_experience()

            if done:
                break

        epsilon = max(epsilon_min, epsilon_decay * epsilon)

        if (episode + 1) % 10 == 0:
            print(f"Episode: {episode + 1}, Reward: {total_reward}")

    env.close()

7. Train the agent:

num_episodes = 100
train_agent(num_episodes)

This is a basic implementation of DQN for the CartPole-v0 environment. You can further optimize and customize the code based on your specific requirements or try it with different environments.