#DQN

This repository is a resource for deep reinforcement learning, including both classic and advanced algorithms implemented in PyTorch. It provides code to aid understanding and experimentation, featuring models like DQN, PPO, and A3C. Actively maintained, the project plans to expand with new algorithms. It supports environments such as CartPole and BipedalWalker and includes installation guides, dependencies, and links to academic papers.

This course provides a hands-on experience in deep reinforcement learning by guiding participants through building algorithms like DQN, SAC, and PPO from scratch. Learn to use tools such as Miniconda and Poetry in a streamlined coding environment with comprehensive resources for independent verification and learning.

This project reimagines the classic Snake game through advanced AI-driven algorithms and performance metrics, transitioning from C++ to Python with an intuitive interface. It enhances gameplay by optimizing the snake's length and movement using solvers such as Hamilton, Greedy, and experimental DQN, evaluated through average length and steps. Designed for AI practitioners, the project supports easy integration with Python 3.6+ and Tkinter, with comprehensive unit testing to ensure durability. Explore AI's impact on strategic gaming applications.

Discover how to transition from DQN to Rainbow in this deep reinforcement learning guide, featuring theoretical insights and practical implementations. Suitable for beginners and experienced users, this tutorial is accessible via Google Colab, even on mobile devices. Essential topics include DoubleDQN, Prioritized Experience Replay, and NoisyNet. Contributions are welcomed through pull requests or issues, emphasizing practical application for mastering AI-driven strategies.

This project features a modular implementation of deep reinforcement learning algorithms using PyTorch. It seamlessly transitions from simple tasks to complex games, incorporating methods like Double DQN, A2C, and PPO. With efficient data generation and hardware optimization, it's suitable for scalable deep learning research. Support is available for robust testing environments such as Breakout and Mujoco. Discover innovative algorithmic insights and performance metrics visualized through detailed learning curves.

This project presents a reinforcement learning application using Deep Q-Learning (DQN) to train the Cartpole system. The system requires precise force applications to keep the pole balanced on a frictionless track. Details include specific hyperparameters like a learning rate of 0.001, batch size of 20, and the use of experience replay. Achieving an average reward of 195.0 over 100 trials qualifies as successful, showcasing efficient balance control. Learn more about performance metrics and see example trials of successful execution.