Population Based Training (in PyTorch with sqlite3). Status: Unsupported

The MacGraph network. An attempt to get MACnets running on graph knowledge

A Julia implementation of choice sequence based PBT, inspired by Hypothesis

A simple PyTorch implementation of Population Based Training of Neural Networks.

Reproducing results from DeepMind's paper on Population Based Training of Neural Networks.

A friendly functional language for finding counterexamples 🎯

Applying Population Based Training on Generative Adversarial Networks.

Population-Based Training (PBT) implementation on ddpg

My attempt to reproduce a water down version of PBT (Population based training) for MARL (Multi-agent reinforcement learning) using DDPPO (Decentralized & distributed proximal policy optimization) from ray[rllib].

A small intro to property-based testing

Applying PBT optimization technique to different domains

The implement of PBT with pytorch (for Reinforcement Learning)

Bug finding challenge for all of the tools around

Property based testing for python

Bird Bioaccumulation (B) and Biotransformation (BT)

Presentación de testing basado en propiedades

Population Based Training of neural networks for multiagent environments

An étude (i.e., study) to practice Property-Based Testing, in Java, using JUnit 5 + jqwik

本项目是一个基于 MuZero 和 Gumbel 搜索构建的三人八子棋（Eight-in-a-Row）AI 训练与对弈系统。该系统采用了 DeepSeek MLA 作为网络骨干，并深度集成了课程学习（Curriculum Learning）、联赛机制（League）、基于种群的训练（PBT）、Engram 情节记忆以及 EfficientZero 的一致性损失等高级强化学习技术。此外，项目支持同步与多进程异步训练，并提供了基于 FastAPI 的实时对弈服务以及基于 WebSocket 的可视化训练监控台。