Inspired by QWOP, I created a walking game where the player must walk by controlling 4 ragdoll properties: thigh angle and knee angle for both left and right knees. These behaviors are controlled using 4 keys: Q, W, O and P. You can try the game out below, as well as the AI. Press Q to move the ragdoll's legs closer together and W to move them ...

This is an experiment in training an RL agent to play the famous game QWOP. This might seem like nothing new. I've trained plenty of RL agents before, and I've even turned HTML5 games into RL environments before.

新AI改用Prioritized DDQN算法，因为这种算法会给学习效率更高的状态增加权重而不是均匀采样，能使新AI迅速学会旧AI已经掌握了的技巧。 并且，新AI的奖励函数去掉了身体高度，膝盖弯曲角度等参数，改成只和前进速度相关。

AI for a QWOP-inspired game. Contribute to whsieh/qwop-ai development by creating an account on GitHub.

2021年3月22日 · 一開始，Liao 使用 OpenAI Gym 強化學習環境來訓練 AI，先設定好遊戲的狀態、操作和獎懲機制。 狀態包括每個身體部位和關節的位置、速度和角度。 操作方式限定為 11 種：4 個 QWOP 按鍵、6 種兩兩按鍵組合以及不按任何鍵。 用來訓練 AI 的算演法是 ACER（具有經驗回放能力的 Actor-Critic）。 這種演算法的優點是，不僅可以從其最近獲得的經驗中學習，也可以學習儲存數據中更早的經驗。 由於 ACER 非常複雜，Liao 使用了別人的實現程式碼 …

Train an AI agent to play QWOP using reinforcement learning and imitation learning. Check out the ACER video summary, DDQN world record video or the write-up on Medium.

Q(wop), also known as qwop-ai, is a learning agent I developed to solve a physics-based walking game. You can find the game here. Use the Q,W,O and P keys to move the ragdoll, and see how far you can get! After taking Intro to AI (CS188), I was inspired to write an AI to tackle the brilliantly frustrating flash game QWOP.

2021年3月16日 · 多年前有款奇葩跑步模拟游戏《qwop》，玩家只需越过障碍，以最快的速度跑完一百米即可。 游戏名的四个字母也并非缩写或者什么奇怪的暗示语，而是对应了操控游戏的四个按键。

2023年6月19日 · In this tutorial, we show you how to use gymnasium and stable baselines with the PPO algorithm to teach a reinforcement learning AI agent to play the web-based game, qwop.

AI agent to play the QWOP game using genetic algorithm - CABLord/qwop-ai