DQN python

DQN (Deep Q-Network) is a reinforcement learning algorithm commonly used for solving control problems. It uses a neural network to approximate the Q-function, which represents the expected future reward for taking a particular action in a given state.

To implement DQN in Python, you can use libraries like TensorFlow or PyTorch. Here's a basic outline of how you could structure your code:

1. Import the necessary libraries:

import numpy as np
import random
from collections import deque
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense
from tensorflow.keras.optimizers import Adam

2. Define the DQN agent class:

class DQNAgent:
    def __init__(self, state_size, action_size):
        self.state_size = state_size
        self.action_size = action_size
        self.memory = deque(maxlen=2000)
        self.gamma = 0.95  # discount rate
        self.epsilon = 1.0  # exploration rate
        self.epsilon_min = 0.01
        self.epsilon_decay = 0.995
        self.learning_rate = 0.001
        self.model = self._build_model()

    def _build_model(self):
        model = Sequential()
        model.add(Dense(24, input_dim=self.state_size, activation='relu'))
        model.add(Dense(24, activation='relu'))
        model.add(Dense(self.action_size, activation='linear'))
        model.compile(loss='mse', optimizer=Adam(lr=self.learning_rate))
        return model

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

    def act(self, state):
        if np.random.rand() <= self.epsilon:
            return random.randrange(self.action_size)
        else:
            return np.argmax(self.model.predict(state)[0])

    def replay(self, batch_size):
        minibatch = random.sample(self.memory, batch_size)

        for state, action, reward, next_state, done in minibatch:
            target = reward
            if not done:
                target = (reward + self.gamma *
                          np.amax(self.model.predict(next_state)[0]))
            target_f = self.model.predict(state)
            target_f[0][action] = target

            self.model.fit(state, target_f, epochs=1, verbose=0)

        if self.epsilon > self.epsilon_min:
            self.epsilon *= self.epsilon_decay

3. Create an instance of the DQNAgent and train it:

state_size = ...
action_size = ...
agent = DQNAgent(state_size, action_size)

# Training loop
for episode in range(num_episodes):
    state = env.reset()
    state = np.reshape(state, [1, state_size])
    done = False
    total_reward = 0

    while not done:
        action = agent.act(state)
        next_state, reward, done, _ = env.step(action)
        next_state = np.reshape(next_state, [1, state_size])
        agent.remember(state, action, reward, next_state, done)
        state = next_state
        total_reward += reward

    agent.replay(batch_size)

    # Print episode statistics or perform other actions if needed

    # Exploration-exploitation trade-off
    if episode % 10 == 0:
        agent.epsilon *= 0.9

This is a basic implementation of the DQN algorithm in Python. You may need to modify it based on your specific problem and environment. Remember to define your own state and action spaces and update the code accordingly.