Reinforcement Learning with Deep Q-Networks (DQN)

The Reinforcement Learning with Deep Q-Networks (DQN) is a Python class that implements the DQN algorithm for reinforcement learning tasks. It allows agents to learn optimal policies through interaction with an environment using Q-learning and deep neural networks.

Usage:

1. Initialize the DQNAgent object with the state shape and action size.
2. Interact with the environment by selecting actions using the act method.
3. Train the agent using the train method with experience tuples.

Features:

• Utilizes deep neural networks to approximate Q-values for state-action pairs.
• Implements epsilon-greedy action selection for exploration and exploitation.
• Supports training with experience replay and target networks for stability.

Requirements:

• Python 3.x
• TensorFlow