deep q learning python code

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Deep Q-Learning (DQN) is a popular reinforcement learning algorithm that combines deep neural networks with Q-learning. It is widely used for solving complex decision-making problems in artificial intelligence. In this tutorial, we will implement a simple Deep Q-Learning algorithm using Python and popular libraries like TensorFlow and OpenAI Gym.
Before you begin, make sure you have the following installed:
You can install the required libraries using the following command:
Deep Q-Learning involves training a neural network to approximate the Q-function, which represents the expected future rewards for taking a particular action in a given state. The Q-network takes the current state as input and outputs Q-values for each possible action. The agent selects the action with the highest Q-value.
The algorithm uses experience replay and target networks to stabilize training. Experience replay stores past experiences in a replay buffer and randomly samples them during training. Target networks are used to prevent instability by keeping a separate network with frozen parameters for updating the Q-values.
Let's implement a simple Deep Q-Learning algorithm using TensorFlow and OpenAI Gym. We'll use the CartPole environment as an example.