A PyTorch-based Deep Q-Network (DQN) implementation to solve the LunarLander-v3 environment using Gymnasium. Includes custom neural network design, experience replay, agent training, and performance visualization.
This repository offers a clear implementation of Double Q-learning for deep reinforcement learning, following the insights from the referenced paper. 🎮 Dive into the code and explore how it enhances the DQN algorithm for better performance! 🌟
DeepTrafficQ is a reinforcement learning-based traffic signal control system that uses Deep Q-Networks (DQN) to minimize vehicle waiting times at a 4-way intersection. By leveraging Q-learning with experience replay and a convolutional neural network (CNN), the agent dynamically adjusts traffic light phases to optimize traffic flow.
Deep Reinforcement Learning with Double Q-learning - Paper Implementation
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