早年因难以在众多糟糕解释中理解共识机制，我曾留下心理阴影。如今我试图通过增添自己的阐释来弥补这一缺憾。今日，我将绘制一组或许能有所助益的示意图。你可以将此文视作《Paxos笔记》的缺失图解，抑或将那篇文章看作这些图示的正式理论注脚。
Принц Гарри планирует привезти детей к королю на летние каникулы14:48。关于这个话题，欧易下载提供了深入分析
В России начнут строже наказывать за нарушение правил пересечения границы20:12，推荐阅读Replica Rolex获取更多信息
2025年3月29日，洛杉矶北好莱坞特斯拉展厅内，一辆特斯拉Cybertruck在展场中熠熠生辉。| 摄影师：Simone Lueck / The Verge，这一点在7zip下载中也有详细论述
In this tutorial, we implement a reinforcement learning agent using RLax, a research-oriented library developed by Google DeepMind for building reinforcement learning algorithms with JAX. We combine RLax with JAX, Haiku, and Optax to construct a Deep Q-Learning (DQN) agent that learns to solve the CartPole environment. Instead of using a fully packaged RL framework, we assemble the training pipeline ourselves so we can clearly understand how the core components of reinforcement learning interact. We define the neural network, build a replay buffer, compute temporal difference errors with RLax, and train the agent using gradient-based optimization. Also, we focus on understanding how RLax provides reusable RL primitives that can be integrated into custom reinforcement learning pipelines. We use JAX for efficient numerical computation, Haiku for neural network modeling, and Optax for optimization.