README.md

metadata

title: Mr. Pong
emoji: 🏓
colorFrom: blue
colorTo: gray
sdk: gradio
sdk_version: 6.24.0
app_file: app.py
short_description: Play table tennis against a PPO agent
python_version: '3.12'
startup_duration_timeout: 30m
models:
  - fromziro/MrPong
tags:
  - reinforcement-learning
  - ppo
  - game

🏓 Mr. Pong

An interactive demo for fromziro/MrPong — a 28,484-parameter actor–critic MLP trained with PPO and self-play for 10M steps to play 2D table tennis.

Three tabs:

• Play Mr. Pong — the policy is exported from the checkpoint and evaluated in your browser (plain JavaScript, ~26K MACs per decision), so the agent's paddle reacts at the same 40 sub-steps/second the physics runs at, with no network latency. The critic's value estimate V(s) is shown live.
• Watch a match — the real PyTorch checkpoint plays a full match against one of the scripted baselines from the model card, rendered to video server-side.
• Benchmark — replays the model card's evaluation protocol over N matches.

The physics environment (pong_engine.py) and the baseline opponents are a 1:1 port of the author's reference inference.py, so behaviour matches the reported numbers.

Runs entirely on CPU: a policy forward pass is a 12→160→160→3 MLP.

Model, environment and baselines by FromZero, Apache-2.0.