项目包含1个核心Python脚本（q_learning_with_pygame.py）实现Q值表更新、ε-greedy策略选择及环境交互逻辑；1份README.md文档说明运行依赖、参数配置与训练效果解读；1个GIF动图直观展示智能体从随机探索到稳定抵达右下角目标点（绿色圆点）的收敛过程，障碍物（中心2,2位置）与碰撞惩罚机制均被清晰呈现。

The study found that targeting large, mature female pythons is key to controlling the population. Optimal hunting conditions include specific moon phases, warmer and more humid weather, and cool ...

文章浏览阅读272次。Q-Learning 是强化学习中最基础的无模型算法，其核心在于通过贝尔曼更新迭代优化状态-动作价值表（Q表）。理解其收敛机制、探索-利用权衡与奖励塑形逻辑，是掌握更复杂RL方法的前提。本文聚焦于工程落地视角，以纯Python+Pygame构建轻量级可视化训练环境，将抽象的Q值更新、ε ...

Modular reinforcement learning pipeline for legged robot locomotion in Genesis, inspired by Unitree pipelines and designed for scalable sim-to-real transfer.

A Double Deep Q-Network (DDQN) reinforcement learning algorithm to train a Battlesnake agent. Several features are used to enhance the algorithm, including vectorized environments, prioritized ...