2 个仓库
Awesome GitHub RepositoriesPopulation-Based Training
An iterative optimization strategy that evolves a population of models by replacing poor performers with mutated versions of top models.
Distinct from Hyperparameter Optimization: Distinct from general hyperparameter optimization: specifically uses a population-based evolutionary approach rather than simple grid or random search.
Explore 2 awesome GitHub repositories matching artificial intelligence & ml · Population-Based Training. Refine with filters or upvote what's useful.
Awesome Population-Based Training GitHub Repositories
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- xviniette/flappylearning
xviniette/FlappyLearning
3,993在 GitHub 上查看FlappyLearning 是一个机器学习沙盒和神经网络训练器，旨在模拟神经进化策略。它提供了一个环境，其中人工代理使用遗传算法进行进化，以优化游戏模拟中的性能。 该系统采用神经进化种群模型，利用前馈神经网络来开发自主游戏代理。它通过基于世代的迭代来管理这些代理的进化，使用基于适应度的选择来确定哪些网络能够存活和繁殖。 该训练框架结合了随机权重变异以探索新行为，并结合了基于精英主义的保留机制，以在世代间保持表现最好的特征。用户可以调整种群规模和变异率等参数来微调学习过程。
Implements a population-based training strategy to evolve neural network weights through mutation and selection.
JavaScriptflappybirdmachine-learningneuroevolution - isaac-sim/isaacgymenvs
isaac-sim/IsaacGymEnvs
2,942在 GitHub 上查看IsaacGymEnvs is a GPU-accelerated physics sandbox and robotics policy training suite designed for reinforcement learning. It serves as a vectorized robotic simulator that runs thousands of parallel environments on GPUs to accelerate the training of neural networks. The project provides a sim-to-real transfer framework that utilizes domain randomization and physics variations to ensure policies trained in simulation are robust enough for deployment on real hardware. It distinguishes itself through a high-performance architecture that uses tensor-based state management to handle observations an
Implements population-based training to iteratively optimize hyperparameters and improve agent performance.
Python
