Awesome GitHub RepositoriesContinuous Control Training

This project is an educational repository of reinforcement learning agents and tutorials implemented using TensorFlow. It provides a practical codebase for both model-free and model-based learning agents, designed to demonstrate how AI agents learn through trial and error. The collection features detailed implementations of various algorithmic approaches, including Deep Q-Networks and Policy Gradient methods. It specifically covers Actor-Critic architectures for continuous and discrete action spaces, alongside Proximal Policy Optimization and Deep Deterministic Policy Gradients. The framewor

CleanRL is a reinforcement learning library and PyTorch framework providing a suite of reproducible implementations for online reinforcement learning algorithms. It serves as a deep reinforcement learning benchmark suite and experiment orchestrator designed for research and agent development across both discrete and continuous action spaces. The project is distinguished by its single-file algorithm implementation approach, which encapsulates each algorithm in a standalone script to eliminate complex class hierarchies. This structure is paired with a system for scheduling and executing large-s

This project is a comprehensive collection of practical code examples and implementation libraries for machine learning. It provides a wide array of reference materials for building supervised, unsupervised, and reinforcement learning algorithms. The repository serves as a multi-domain resource, featuring specific implementation suites for financial AI, Bayesian statistical modeling, and deep learning architectures. It includes a framework for training intelligent agents using policy gradients and actor-critic models, as well as practical guides for fine-tuning transformers and utilizing larg

This is a PyTorch-based toolkit for training reinforcement learning agents, providing implementations of standard and hierarchical deep RL algorithms. It is designed as a library for deep reinforcement learning research and experimentation, supporting both discrete and continuous control tasks through a collection of algorithm implementations. The project distinguishes itself by offering a hierarchical reinforcement learning framework that decomposes complex long-horizon tasks into manageable sub-goals using meta-controllers and lower-level policies. It also includes a Hindsight Experience Re

本项目是一个深度强化学习课程，提供用于掌握基于神经网络的代理的教育材料与实现练习。它作为一个框架，用于构建基于价值与基于策略的方法的参考版本，以解决序列决策问题。 该项目为连续控制模拟与多智能体强化学习提供了具体实现，其中代理被训练在共享环境中合作或竞争。它包含一个用于通过 REINFORCE 等方法优化代理行为的策略梯度框架。 能力涵盖了广泛的优化算法，包括深度 Q 学习、确定性策略梯度以及用于马尔可夫决策过程（MDP）建模的动态规划。该系统支持各种训练领域，如机器人导航、金融交易自动化与基于物理的模拟。 材料以一系列 Jupyter Notebooks 的形式交付。

DRL is a curated educational resource that teaches deep reinforcement learning through a structured series of lectures and videos. It covers the three main families of reinforcement learning methods: actor-critic architectures, value-based algorithms like Q-learning and DQN, and policy-based techniques that directly optimize an agent's action-selection strategy. The curriculum extends beyond these core topics to include imitation learning, multi-agent training, and methods for handling continuous action spaces. Content is organized as markdown-driven documentation that generates static, navig

DRL-robot-navigation is a deep reinforcement learning platform and robotic simulation framework designed to train autonomous mobile robots for collision-free path planning. It uses neural network policies and physics-engine simulation environments to teach robots how to navigate toward target coordinates while avoiding obstacles. The software trains continuous control policies using twin delayed deep deterministic policy gradients over continuous state and action spaces. Training is guided by scalar reward signals derived from target proximity and obstacle avoidance distances. System compone