5 个仓库
Awesome GitHub RepositoriesReinforcement Learning Training Loops
Specialized training processes that include state-transition recording and experience replay for RL agents.
Distinct from Neural Network Training Frameworks: Distinct from Neural Network Training Frameworks: specifically handles the episodic and transition-based nature of RL learning.
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Awesome Reinforcement Learning Training Loops GitHub Repositories
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- morvanzhou/reinforcement-learning-with-tensorflow
MorvanZhou/Reinforcement-learning-with-tensorflow
9,464在 GitHub 上查看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
Stores state transitions and updates neural networks to optimize action selection through experience replay.
Pythona3cactor-criticasynchronous-advantage-actor-critic - suragnair/alpha-zero-general
suragnair/alpha-zero-general
4,471在 GitHub 上查看This project is a reinforcement learning framework and game AI engine designed for training adversarial agents in two-player turn-based games. It implements a training loop that utilizes self-play and Monte Carlo Tree Search to produce neural networks capable of predicting board strength and move probabilities. The system decouples the reinforcement learning engine from specific game rules through an abstract game logic interface, allowing for the definition of custom game rules, win conditions, and board representations. It supports integration with various deep learning frameworks to serve
Implements a complete self-play reinforcement learning loop for training adversarial agents in turn-based games.
Jupyter Notebook - simoninithomas/deep_reinforcement_learning_course
simoninithomas/Deep_reinforcement_learning_Course
3,903在 GitHub 上查看该存储库是一个通过结构化、动手编码练习来学习深度强化学习的教育课程。它提供了一个框架，用于构建和训练自主智能体，这些智能体通过与模拟环境交互并接收迭代反馈来学习执行任务。 该项目涵盖了使用深度神经网络函数逼近、时序差分学习和基于梯度的策略优化来实现决策模型。它强调使用经验回放缓冲区和向量化环境模拟来稳定训练，并加速马尔可夫决策过程中的数据收集。 除了核心训练技术外，该材料还包括用于根据标准化基准评估智能体性能以及在协作研究环境中共享训练模型的资源。该存储库旨在与常见的机器学习框架配合使用，为开发智能智能体提供了一套全面的教程和实现方案。
Provides structured training loops for reinforcement learning agents, including state-transition recording and experience replay.
Jupyter Notebooka2cactor-criticdeep-learning - unitreerobotics/unitree_rl_gym
unitreerobotics/unitree_rl_gym
3,378在 GitHub 上查看Unitree RL Gym is an integrated software framework designed for the simulation, training, and deployment of motion control policies for legged robotic platforms. It provides a comprehensive environment for developing locomotion strategies for quadruped and humanoid robots by leveraging reinforcement learning algorithms within high-fidelity physics-based simulations. The platform distinguishes itself by offering an end-to-end workflow that bridges the gap between virtual training and physical execution. It includes specialized tools for mapping raw sensor data into normalized state-space obser
Iteratively executes control policies against reward functions to optimize neural network weights based on simulated state transitions.
Python - leggedrobotics/legged_gym
leggedrobotics/legged_gym
3,022在 GitHub 上查看Legged Gym is a high-performance simulation platform and toolkit engineered for training autonomous robotic agents in complex, physics-based environments. It provides a comprehensive framework for developing legged locomotion control policies, enabling robots to learn movement strategies for navigating uneven terrain and managing physical disturbances through reinforcement learning. The platform distinguishes itself by utilizing hardware-accelerated physics and headless execution to maximize computational throughput during training. It incorporates a domain randomization pipeline that injects
Provides reinforcement learning training loops to optimize robotic agent policies through iterative simulation, hardware configuration, and performance evaluation.
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