**Reinforcement Learning (Deep Q-Networks) Overview:**

Reinforcement Learning (RL) is a type of machine learning where an agent learns to make sequences of decisions (actions) in an environment to maximize a cumulative reward signal. In this example, we'll implement a Deep Q-Network (DQN), a popular RL algorithm, to train an agent to play a simple game.

**Example Using a Dataset:**

**Step 1: Import Libraries**

import numpy as np
import matplotlib.pyplot as plt
import gym
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense
from tensorflow.keras.optimizers import Adam

**Step 2: Create the Environment**

# Create the CartPole environment
env = gym.make("CartPole-v1")
state_size = env.observation_space.shape[0]
action_size = env.action_space.n

**Step 3: Define the Deep Q-Network (DQN) Model**

# Create a sequential model
model = Sequential([
    Dense(24, input_shape=(state_size,), activation='relu'),
    Dense(24, activation='relu'),
    Dense(action_size, activation='linear')
])

# Compile the model
model.compile(loss='mse', optimizer=Adam(learning_rate=0.001))

**Params That Can Be Changed**
1. **Model architecture**: You can modify the number of layers and units in the neural network.
2. **Learning rate**: Adjust the learning rate in the optimizer to control the update rate of the Q-values.

**Step 4: Define the DQN Agent**

class DQNAgent:
    def __init__(self, state_size, action_size):
        self.state_size = state_size
        self.action_size = action_size
        self.memory = []  # Experience replay memory
        self.gamma = 0.95  # Discount factor
        self.epsilon = 1.0  # Exploration rate (initial)
        self.epsilon_min = 0.01  # Minimum exploration rate
        self.epsilon_decay = 0.995  # Exploration rate decay
        self.model = model

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

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

    # Implement the experience replay mechanism (not shown here)

    def train(self, batch_size):
        # Implement Q-learning update (not shown here)

**Step 5: Train the DQN Agent**

# Initialize the DQN agent
agent = DQNAgent(state_size, action_size)

# Training parameters
n_episodes = 1000
batch_size = 64

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

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

        # Store the experience in the agent's memory
        agent.remember(state, action, reward, next_state, done)

        # Update the state for the next iteration
        state = next_state

    # Train the agent using experience replay (not shown here)

    # Decay exploration rate
    if agent.epsilon > agent.epsilon_min:
        agent.epsilon *= agent.epsilon_decay

    print(f"Episode {episode + 1}, Total Reward: {total_reward}")

**Explanation:**

1. We import the necessary libraries, including NumPy for numerical operations, Matplotlib for visualization, OpenAI Gym for RL environments, and TensorFlow for neural networks.

2. We create the CartPole environment from OpenAI Gym, which simulates a cart balancing a pole. We obtain the state and action sizes from the environment.

3. We define a DQN model using TensorFlow's Keras API. The model consists of fully connected layers with ReLU activation in between. It's a Q-value estimator, where the output represents the expected rewards for each action.

4. We create a `DQNAgent` class that encapsulates the DQN agent's behavior. The agent has methods for acting, remembering experiences, and training.

5. In the training loop, the agent interacts with the environment for a specified number of episodes. It selects actions based on an epsilon-greedy policy, stores experiences, and updates its Q-values using Q-learning.

6. The exploration rate (epsilon) starts high and gradually decays to encourage exploration early in training.

7. The agent's training process includes experience replay and Q-learning updates, which are essential parts of the DQN algorithm but are not shown in this simplified example.

Reinforcement learning with DQN is a powerful technique for training agents to make sequential decisions. The above example demonstrates a simplified version of DQN for a basic environment. In practice, more complex RL problems require additional techniques like experience replay, target networks, and more advanced algorithms.