[Enhancement] Add Doom-Env - Doom Slayer goes to OpenEnv School to learn RL

[AlirezaShamsoshoara]

Add Doom Environment with ViZDoom Integration

⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⣠⢾⠍⡉⠉⠙⣿⣆⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀
⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⢀⣠⣴⠾⠿⠽⢷⣶⣤⡀⠀⠀⠀⠀⠀⠀⠀⢀⣟⡟⣠⣿⣶⡀⣷⡻⡄⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀
⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⢠⠴⡟⡋⡀⠀⣀⣀⠀⠀⠉⠛⣦⡀⠀⠀⠀⠀⠀⠀⢿⣅⣽⣿⣿⣷⣿⣿⠃⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀
⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⡴⠃⢀⢾⣿⣿⣿⣯⣬⣽⣿⣀⡀⠈⠙⣆⠀⠀⠀⠀⢀⣸⣯⣿⣾⡷⢻⣿⠋⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀
⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⣜⢁⠁⣾⡿⣙⠿⣯⣭⣍⣹⠼⠋⠁⣴⠀⢘⣧⠀⠀⡴⢛⣭⢟⠽⠋⢠⣼⠁⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀
⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⢸⢻⠘⢸⡿⢷⣬⣧⡀⠀⠀⠀⢀⣤⠾⢿⡇⠘⣿⡆⣸⠛⣿⡿⣟⡀⠀⡾⡇⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀
⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⢸⣾⣦⣿⣿⡄⠈⢿⢿⣷⣶⡾⠋⠁⠀⣸⠇⡰⠛⢷⣷⣻⡿⠺⣿⣿⠽⠋⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀
⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⣀⣀⣀⣀⣀⠀⣴⠏⠀⣿⠙⢻⣿⣄⠈⠀⠸⠀⠉⠀⣠⣾⠟⢀⣧⡇⠀⢽⣿⣿⣬⣼⣿⡀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀
⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⣰⠚⣿⣿⣿⣿⣿⡿⠟⢛⣰⣿⣧⣷⣝⡿⣷⣞⢷⣄⣲⣾⣿⡃⢰⡿⡟⢀⣴⣿⣿⣿⣯⡿⠿⣿⣶⣤⣀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀
⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⣠⠞⢁⣼⣿⣿⣿⡟⠋⠁⣉⣽⣿⣿⣿⣿⣿⣽⣯⣿⡄⠉⠁⢷⣬⣹⣿⣿⣤⡾⠁⣸⣿⣿⡟⠁⠀⠀⢹⣿⣿⣷⡆⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀
⠀⠀⠀⠀⠀⠀⠀⠀⠀⢀⣾⡷⠞⣫⣾⣿⣿⣿⣧⡀⣤⠀⠈⣻⣿⣿⣿⣿⣿⣿⣿⣷⣖⠀⠘⢿⣿⣿⣿⣿⣿⣿⣿⣿⣿⠃⠋⠻⢤⣅⡺⢦⡀⠳⡄⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀
⠀⠀⠀⠀⠀⠀⠀⠀⣰⣿⣯⣴⠞⠁⣀⣿⣿⣿⣿⣷⣄⣤⠤⢊⣿⣿⣿⣿⣿⣿⣿⣿⣯⣴⣴⣶⣿⣿⠟⣸⣿⣿⣿⣿⡏⡆⠀⢠⣤⣠⣥⠀⡟⣶⣿⡄⠀⠀⠀⠀⠀⠀⠀⠀⠀
⠀⠀⠀⠀⠀⠀⠀⠀⣽⡿⣿⣏⡀⠀⠹⣟⣿⡿⣿⣿⣋⣶⣺⡽⣿⣏⣅⠛⠂⠴⠶⠿⠿⠃⠈⠉⠻⣷⣶⣿⣿⣿⣿⡿⠀⣿⡄⠈⣷⣮⠙⢀⡿⠘⢻⣇⠀⠀⠀⠀⠀⠀⠀⠀⠀
⠀⠀⠀⠀⠀⠀⠀⠀⢸⣧⢻⡶⠀⠀⠘⢿⣿⣿⣿⣿⣿⠋⠉⠀⠀⠉⠻⠿⠶⠶⠶⠦⠴⠞⠛⠷⠗⠈⠛⢿⣿⣿⡿⢁⣼⠯⠄⠀⠀⠀⣠⡞⠁⣠⣾⣿⠀⠀⠀⠀⠀⠀⠀⠀⠀
⠀⠀⠀⠀⠀⠀⠀⠀⢸⣻⣾⣷⡀⢐⠀⣿⣿⣿⣿⣿⠁⠀⠠⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⡁⠰⣾⣿⠀⠀⠈⢻⣿⣅⢿⣇⠀⠀⠀⠀⢀⣿⡟⠀⡷⢿⢿⠀⠀⠀⠀⠀⠀⠀⠀⠀
⠀⠀⠀⠀⠀⠀⠀⠀⠀⠙⣿⡎⣤⣌⡰⣿⣿⣿⣿⣟⠀⠠⠀⢀⡀⠀⠂⠀⠀⠉⠉⠉⠈⠉⠙⢾⣭⡤⠂⠀⠀⠹⣿⣎⣿⣶⣒⣿⣷⣿⣯⣮⡵⣿⣾⣿⡀⠀⠀⠀⠀⠀⠀⠀⠀
⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠈⢻⢿⠛⣿⠛⠛⢿⣿⣿⣃⢀⣀⣀⠀⣀⣤⣾⠓⠶⠖⠷⣤⣄⡀⠀⠀⠀⠀⠀⠀⢠⣿⣿⣾⣿⣿⣿⠍⣩⣉⣿⡆⠰⣿⣭⡇⠀⠀⠀⠀⠀⠀⠀⠀
⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⢀⡸⠾⢴⡇⠀⠀⢸⣿⣿⣯⣭⣿⣿⣿⡿⠛⠛⠛⠛⠛⠛⠛⠟⠻⣷⣶⣴⣶⣮⡴⠫⢾⣿⣿⣟⠉⣹⣿⣿⣿⣿⣷⣄⠸⢿⡇⠀⠀⠀⠀⠀⠀⠀⠀
⠀⠀⠀⠀⠀⠀⠀⠀⠀⣴⠋⡽⠁⡾⠀⠀⠀⣼⣿⣧⣁⣴⣶⠾⢿⣿⡶⠀⠒⠒⠂⠀⠀⠀⣰⣾⣧⣌⣉⠙⠂⢠⢿⣿⣿⣫⡿⠿⠋⠉⠈⠙⢻⣽⢧⠀⣽⣄⠀⠀⠀⠀⠀⠀⠀
⠀⠀⠀⠀⠀⠀⢀⣴⡻⣻⣼⣿⣰⠇⠀⠀⠀⠉⣁⣿⣟⢉⣼⣶⣶⡿⠿⣿⡟⠛⠛⠛⣷⣾⢿⣯⣤⣤⡉⠳⡶⢋⡞⣿⣿⣇⠀⠀⠙⠀⠀⠀⢀⣿⣫⠇⣈⣁⣣⡀⠀⠀⠀⠀⠀
⠀⠀⠀⠀⠀⣠⠎⠉⣰⣿⣿⣿⠉⢲⣤⠀⠀⠾⣿⣿⣳⣜⢿⡟⠫⢠⣶⣾⣷⣤⣤⣼⣯⡤⣤⣀⣻⠻⣿⣦⣠⠞⣼⣿⡿⢿⣤⡸⣷⣦⣤⣴⣿⣿⣯⠼⢥⣈⣿⡗⠶⢤⡀⠀⠀
⠀⠀⠀⠀⢰⡃⠀⣼⣿⣿⣿⣿⣷⣤⣁⣀⣤⣾⣿⣿⣿⣿⣿⣿⠷⣾⣟⣀⣫⣄⣀⣀⣠⣄⠘⢿⡤⠴⣷⡿⠃⠘⡽⣿⣃⠘⣿⣿⣿⣿⣿⣿⣿⠿⡿⠟⠀⠘⣝⢿⡆⠀⠻⣦⡀
⠀⠀⠀⢀⡏⢀⣾⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⡿⢳⣿⣯⣿⣿⣿⡿⣾⣿⡏⠙⣿⡉⠙⡍⠉⢿⣟⣴⠶⠾⢿⣟⠷⣿⡿⠿⣷⣾⡇⢻⣿⣿⡏⡴⠞⠻⣞⡍⠙⢫⣿⣧⠀⠀⠘⠃
⠀⠀⢀⣾⢿⡾⢷⣿⣿⠋⣿⣿⣿⣿⣿⣿⣿⡗⣼⢿⣿⣿⠘⣿⣿⣷⣼⣣⣶⠾⠿⠛⠶⣦⠚⣠⣴⣿⣿⠋⢰⣼⣯⠁⠐⢺⣿⣿⣮⣿⣿⣿⡟⠂⢀⣽⡓⡀⠒⢹⣿⠇⠀⣤⡀
⠀⠀⣿⠿⣾⣳⣼⣏⠛⠛⢿⣯⣶⣿⢋⣼⣿⢱⣟⣷⣮⠻⣷⠘⠿⣿⣭⣉⡉⣠⣤⣤⣄⣉⣉⣁⣾⡿⠟⣠⣾⡏⣡⠎⠀⢸⣿⡌⣿⣿⣿⣿⣟⡂⠠⢿⡅⢨⡏⣾⣟⠀⠀⠈⠁
⠀⢸⡿⠓⢀⣿⣿⣿⡷⣦⣼⠟⣹⡵⠛⢳⢟⣾⣿⣿⡿⠀⣿⠄⡀⣿⣯⠙⣿⡟⠛⢛⠛⣿⣿⡏⢉⡇⠀⢯⣿⡇⡅⢴⠀⢸⣾⡇⠸⣟⠹⣿⢿⡏⢰⣿⣆⣈⠁⣽⣿⠀⠀⠀⠀
⢀⡖⠘⠃⢠⣿⣯⡟⠻⣿⣻⡟⠃⠀⠀⠸⣿⢿⣿⣿⣿⣾⣿⣿⣿⣿⣿⣧⢸⣧⣤⣭⣤⣿⣿⡔⢿⣿⡿⣿⣿⣿⣷⣤⣠⣿⣿⠃⠀⣿⣇⣿⣿⣷⣿⣿⠿⢽⣷⣩⣿⠀⠀⠀⠀
⣾⠁⣠⠹⣿⣿⡟⠻⣶⣿⢻⡇⠀⠀⠀⠀⠈⢹⡿⣿⣿⣿⣿⢟⣟⢿⢿⣿⣿⡷⠶⠶⠶⠈⢯⡻⡄⢻⣿⢀⠙⢿⣿⣿⣷⡟⠁⢀⣴⢟⣺⣿⣿⣿⣥⣽⣶⣄⣈⣿⣿⠀⠀⠀⠀
⣭⠎⠿⢠⡟⢿⣿⣷⣽⣿⣼⡇⠀⠀⠀⠀⢠⣿⢿⡛⢿⡿⣿⡾⣿⡇⢠⣿⣿⡇⠀⠀⠀⠀⣈⢻⡖⢸⣿⢿⣾⢏⠟⠛⢿⣧⣀⣸⣴⡿⢻⣿⣻⣍⠉⣉⠛⣛⠛⠛⢿⡷⠀⠀⢀
⢳⣶⠖⠈⢿⣿⣛⠹⣿⣿⢸⡃⠀⠀⠀⣠⠟⣩⠞⠀⠈⣿⡟⣵⡿⠃⣼⣿⣿⠁⠐⠀⠘⠃⠉⣸⣇⠀⠹⣦⢻⣟⠀⠀⠀⠹⣿⣴⣯⣼⣿⣿⣿⣿⡄⣿⡀⢿⣰⡇⢸⡇⠀⠠⠋
⠸⣹⡶⠀⢸⣿⣿⣿⣷⣛⢻⡇⠀⠀⢠⡷⠃⠁⠀⠀⠀⣿⠸⣿⠀⠠⣿⣿⣧⡀⠀⠀⠀⠀⢰⣿⣄⠁⠀⣹⢦⣿⣦⠀⠀⠀⣿⣿⣿⡏⣿⡏⡛⠟⢲⣶⢶⣾⣷⡭⣸⡴⠊⠀⠀
⠀⢹⡄⣄⡘⣿⣿⣿⣿⠹⡿⠁⠀⠀⣿⠇⠀⠀⠀⠀⡶⠘⡇⣿⡃⠂⣻⣿⣿⣷⡄⠀⠀⠀⢸⣿⣝⡓⢰⣿⣾⡏⣿⣦⠀⠀⢹⣾⣿⡎⢰⣷⣓⠀⣼⣿⢸⣿⢹⡆⢿⠇⠀⠀⠀
⠀⠀⠙⠻⣿⣿⠧⠭⠭⠟⠁⠀⠀⣸⡽⢐⠀⠀⠀⢸⣇⣸⡷⣿⠃⠀⢿⣿⣿⣿⣿⣿⣷⣦⣿⣿⣯⡟⢺⣿⣿⣇⣸⡿⡇⠀⢀⡟⣿⡧⢸⣷⡌⢀⣿⣿⣼⣿⠮⣿⠋⠀⠀⠀⠀
⠀⠀⠀⠀⠀⠈⠁⠀⠀⠀⠀⠀⢀⣟⣷⡿⠀⠀⠀⠀⠉⢸⡇⡷⠀⢀⠈⠻⣿⣿⢿⣿⢿⣿⣿⣿⣿⣷⣾⣿⣿⡇⠉⠀⠀⠀⢸⡇⢸⣿⡾⡿⣧⣼⣿⠵⣿⣇⡾⠁⠀⠀⠀⠀⠀
⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⢸⣾⣼⡛⢸⡄⠀⠀⠀⢸⣧⢳⣀⠀⠀⠀⣿⢋⡟⠈⢧⢻⣿⣿⣿⣿⣿⣿⣿⣷⡀⠀⠀⠀⠈⡇⠀⢯⡇⠀⠉⠙⠙⠉⠉⠋⠀⠀⠀⠀⠀⠀⠀
⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⢸⣿⣿⢹⡸⣷⠀⠀⠀⠄⠻⢷⣄⣀⢀⣼⣣⠟⠀⠀⠈⢣⠹⣿⣿⣿⣿⣿⣿⠿⢷⡄⠀⠀⠀⠀⠀⢸⡇⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀
⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⢸⣿⣿⡈⢁⣽⣷⡆⠀⠀⠀⠀⢈⣽⣿⡿⠃⠀⠀⠀⠀⠀⠙⣌⢻⣿⣿⣿⣿⠀⠈⢿⣦⠓⠀⠀⠀⣸⡇⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀
⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⣸⠚⢿⣷⣿⣿⣯⣻⡄⠀⠀⢀⣾⠟⡿⠁⠀⠀⠀⠀⠀⠀⠀⠈⢦⡻⣿⣿⣷⡀⢠⣾⣫⡿⣬⡃⠆⠛⣧⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀
⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⢠⣻⣷⢾⣟⠛⠁⠉⢻⣿⣆⣠⡾⢿⣿⠁⠀⠀⠀⠀⠀⠀⠀⠀⠀⠘⣇⣸⣿⣿⣾⣾⣿⠇⠀⠈⢙⡟⠿⢻⣆⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀
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⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⣸⣉⣿⡳⠀⠀⠈⠁⠀⠀⠈⢿⡄⣿⡇⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠘⣿⢿⣿⣿⣿⣷⣄⠀⠀⠀⢀⡀⠘⣿⡇⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀
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Summary

This PR introduces a new Doom environment for OpenEnv, wrapping the ViZDoom platform to provide visual reinforcement learning capabilities for Doom-based scenarios. The environment supports multiple scenarios, configurable resolutions, discrete/continuous action spaces, and includes comprehensive documentation with visual examples.

Overview

The Doom environment (doom_env) integrates ViZDoom - a Doom-based AI research platform - into the OpenEnv framework, enabling agents to:

• Learn from visual observations (RGB or grayscale screen buffers)
• Execute actions in 3D game environments
• Train on multiple built-in scenarios (basic, deadly_corridor, defend_the_center, etc.)
• Track game variables (health, ammo, kills)
• Deploy via Docker or run locally

What's Included

Core Implementation

• models.py - Data models for actions and observations

  • DoomAction: Action dataclass with support for discrete actions or button combinations
  • DoomObservation: Observation dataclass with screen buffer, game variables, rewards, metadata

• client.py - HTTP client for connecting to Doom servers

  • DoomEnv: Full-featured client with rendering support (OpenCV/matplotlib)
  • Automatic numpy type conversion for JSON serialization
  • Client-side rendering with render() method

• server/doom_env_environment.py - Core ViZDoom wrapper

  • Configurable scenarios, resolutions, screen formats
  • Discrete actions and button combinations support
  • Episode management and state tracking

• server/app.py - FastAPI server application

  • Environment variable configuration (scenario, resolution, format)
  • Web interface integration
  • Health check endpoint

Docker Support

• server/Dockerfile - Standalone Docker image
  • Based on python:3.11-slim
  • Includes all ViZDoom system dependencies
  • Configurable via environment variables
  • Works for both local builds and HuggingFace deployment
  • Build command: docker build -t doom-env:latest -f src/envs/doom_env/server/Dockerfile src/envs/doom_env

Documentation

• README.md - Comprehensive environment documentation

  • Quick start guides
  • Scenario gallery with descriptions
  • API reference
  • Configuration options
  • Deployment instructions
  • Visual examples with ASCII art Doom Slayer

• GIF_GENERATION.md - Guide for generating scenario GIFs

  • Step-by-step instructions
  • Example commands
  • Troubleshooting tips

• TEST_PLAN.md - Comprehensive test strategy (future implementation)

  • 67 planned test cases
  • Test categories and fixtures
  • Success criteria

Utilities

• generate_gifs.py - Script to generate scenario visualization GIFs

  • Supports all ViZDoom scenarios
  • Configurable steps, resolution, FPS
  • Automatic output to assets/ directory

• example.py - Example usage script (in examples directory)

  • Demonstrates basic usage
  • Docker and local modes
  • Rendering examples

• doom_visualizer.py - Real-time game visualizer (in examples directory)

  • OpenCV-based visualization with keyboard controls
  • Matplotlib fallback
  • Auto-scaling for different resolutions
  • Interactive controls (arrows, space to shoot)

Assets

• assets/doom_slayer_at_openEnv_school.png - Custom Doom Slayer artwork
• assets/README.md - Assets directory documentation

Key Features

1. Multiple Scenarios

• Basic - Simple target shooting (beginner-friendly)
• Deadly Corridor - Navigate corridor while avoiding fireballs
• Defend the Center - Survival mode defending against monsters
• Defend the Line - Protect a line from advancing enemies
• Health Gathering - Navigate maze collecting health packs
• My Way Home - Navigation to goal location
• Predict Position - Predict enemy positions
• Take Cover - Strategic cover-based combat

2. Flexible Configuration

Environment Variables:

DOOM_SCENARIO=basic                 # Scenario selection
DOOM_SCREEN_RESOLUTION=RES_640X480  # Resolution (160x120 to 1024x768)
DOOM_SCREEN_FORMAT=RGB24            # RGB24 or GRAY8
DOOM_WINDOW_VISIBLE=false           # Show game window
ENABLE_WEB_INTERFACE=true           # Enable /web UI

3. Action Spaces

• Discrete Actions: Simple integer action IDs (0-3 for basic scenario)
• Button Combinations: Full control with custom button lists

4. Rendering Options

• Web Interface - Browser-based UI at /web endpoint
• Client-side Rendering - OpenCV or matplotlib visualization
• RGB Array Mode - Return numpy arrays for custom processing

5. Docker Deployment

Local Build:

docker build -t doom-env:latest -f src/envs/doom_env/server/Dockerfile src/envs/doom_env
docker run -p 8000:8000 -e DOOM_SCREEN_RESOLUTION=RES_640X480 doom-env:latest

HuggingFace Deployment:

cd src/envs/doom_env
openenv push

Technical Details

Architecture

• Client-Server Model: HTTP-based communication via OpenEnv framework
• ViZDoom Integration: Native Python bindings to Doom engine
• Screen Buffer Format: Flattened RGB/grayscale arrays for efficient transmission
• State Management: Episode tracking, step counting, game variables

Fixed Issues

1. Docker Build for HuggingFace

   • Fixed build context to work with both local and HF deployments
   • Changed from copying src/core/ to installing openenv-core via pip
   • Updated paths: WORKDIR /app/env, COPY . ., pip install -e .

2. Environment Variable Configuration

   • server/app.py now reads DOOM_* environment variables
   • Docker resolution changes take effect at runtime
   • Proper defaults for all configuration options

3. JSON Serialization

   • Added numpy type conversion in client.py::_step_payload()
   • Handles np.int64, np.float32, numpy arrays
   • Filters out None values from payload

4. Rendering Window Size

   • doom_visualizer.py auto-scales windows based on resolution
   • Uses cv2.resize() with INTER_NEAREST for pixel art preservation
   • Target window size: 1024px width with appropriate scaling

5. OpenEnv Validation

   • Restructured server/app.py to follow snake_env pattern
   • Added main() function with proper if __name__ == "__main__" block
   • Passes openenv validate for multi-mode deployment

Dependencies

Python Packages (from pyproject.toml):

dependencies = [
    "openenv-core>=0.1.0",
    "fastapi>=0.115.0",
    "pydantic>=2.0.0",
    "uvicorn[standard]>=0.24.0",
    "requests>=2.31.0",
    "vizdoom>=1.2.0",
    "numpy>=1.19.0",
]

Optional:

• opencv-python>=4.5.0 - For client-side rendering
• matplotlib>=3.3.0 - Rendering fallback
• imageio>=2.9.0 - For GIF generation

System Dependencies (for ViZDoom):

• build-essential, cmake
• libboost-all-dev
• libsdl2-dev, libfreetype6-dev
• OpenGL libraries (libgl1-mesa-dev, libglu1-mesa-dev)

File Structure

doom_env/
├── __init__.py                           # Module exports
├── models.py                             # DoomAction, DoomObservation
├── client.py                             # DoomEnv HTTP client
├── README.md                             # Main documentation
├── GIF_GENERATION.md                     # GIF generation guide
├── TEST_PLAN.md                          # Test strategy
├── openenv.yaml                          # OpenEnv manifest
├── pyproject.toml                        # Dependencies
├── uv.lock                               # Locked dependencies
├── generate_gifs.py                      # GIF generation script
├── assets/                               # Generated GIFs and images
│   ├── doom_slayer_at_openEnv_school.png
│   └── README.md
└── server/
    ├── __init__.py
    ├── doom_env_environment.py           # ViZDoom wrapper
    ├── app.py                            # FastAPI server
    └── Dockerfile                        # Docker image

examples/
├── doom_example.py                       # Basic usage example
└── doom_visualizer.py                    # Interactive visualizer

Usage Examples

Basic Usage

from doom_env import DoomEnv, DoomAction

# Connect to server
client = DoomEnv(base_url="http://localhost:8000")

# Reset environment
result = client.reset()
print(f"Initial health: {result.observation.game_variables[0]}")

# Take actions
for _ in range(100):
    action = DoomAction(action_id=1)  # Move left
    result = client.step(action)

    if result.observation.done:
        print(f"Episode finished! Total reward: {result.reward}")
        break

client.close()

Docker Mode

from doom_env import DoomEnv, DoomAction

# Start container automatically
client = DoomEnv.from_docker_image("doom-env:latest")

result = client.reset()
result = client.step(DoomAction(action_id=0))

client.close()

With Rendering

client = DoomEnv.from_docker_image("doom-env:latest")
result = client.reset()

for _ in range(100):
    result = client.step(DoomAction(action_id=1))
    client.render()  # Display the game

client.close()

Visual Examples

The environment includes a custom Doom Slayer ASCII art and supports generating GIFs of all scenarios:

python generate_gifs.py basic --steps 500 --resolution RES_640X480

Testing

Comprehensive test plan covering:

• 15 Model Tests - Data validation, serialization
• 18 Environment Tests - ViZDoom wrapper functionality
• 20 Client Tests - HTTP client, rendering, serialization
• 14 Integration Tests - End-to-end, Docker, performance

Note: Test implementation deferred to future PR

Documentation Updates

• Added Doom environment card to docs/environments.md
• HuggingFace Space: https://huggingface.co/spaces/Crashbandicoote2/doom_env
• Comprehensive README with examples and API reference
• GIF generation guide for creating scenario visualizations

Validation

• Passes openenv validate for multi-mode deployment
• Docker builds successfully: docker build -t doom-env:latest -f src/envs/doom_env/server/Dockerfile src/envs/doom_env
• Runs locally: python -m doom_env.server.app
• Deploys to HuggingFace: openenv push works correctly
• Al environment variables respected at runtime
• Cient-server communication verified with example scripts

Related Issues

• Implements visual RL environment support for OpenEnv
• Adds multi-scenario support with 8 built-in Doom scenarios
• Add examples for RL training in the example folder

Future Work

• Implement comprehensive test suite
• Add support for custom WAD files
• Implement multi-agent scenarios
• Add more advanced scenarios (deathmatch, CTF)

Links

• HuggingFace Space: https://huggingface.co/spaces/Crashbandicoote2/doom_env
• ViZDoom Documentation: http://vizdoom.cs.put.edu.pl/
• ViZDoom GitHub: https://github.com/mwydmuch/ViZDoom

Breaking Changes

None - This is a new environment addition.

Checklist

• Code follows project style guidelines
• Documentation is comprehensive and clear
• Docker builds successfully (local and HuggingFace)
• Environment validates with openenv validate
• Example scripts provided and tested
• README includes usage examples
• Added to environments documentation
• HuggingFace Space deployed and working

---

[Copilot]

Pull request overview

This PR adds a comprehensive Doom environment to OpenEnv by integrating ViZDoom, a Doom-based AI research platform for visual reinforcement learning. The implementation provides visual observations, multiple built-in scenarios, flexible action spaces (discrete/continuous), and comprehensive tooling for development and deployment.

Key Changes:

• Complete ViZDoom wrapper with 8 built-in scenarios (basic, deadly_corridor, defend_the_center, etc.)
• HTTP client-server architecture with FastAPI backend and Python client
• Docker deployment support with configurable environment variables
• Comprehensive test suite (65 tests across models, environment, client, and integration)
• Visualization tools (web interface, OpenCV/matplotlib rendering)
• Documentation with scenario gallery, deployment guides, and troubleshooting

Reviewed changes

Copilot reviewed 28 out of 39 changed files in this pull request and generated 18 comments.

Show a summary per file

File	Description
src/envs/doom_env/models.py	Data models for actions and observations with screen buffer support
src/envs/doom_env/client.py	HTTP client with rendering capabilities and numpy type conversion
src/envs/doom_env/server/doom_env_environment.py	Core ViZDoom wrapper implementing OpenEnv Environment interface
src/envs/doom_env/server/app.py	FastAPI application with environment variable configuration
src/envs/doom_env/server/Dockerfile	Standalone Docker image with ViZDoom system dependencies
src/envs/doom_env/pyproject.toml	Package configuration with all required dependencies
src/envs/doom_env/tests/*.py	Comprehensive test suite (4 test files, 65 total tests)
examples/doom_example.py	Example demonstrating Docker and local usage modes
examples/doom_visualizer.py	Interactive visualizer with keyboard controls (OpenCV)
src/envs/doom_env/generate_gifs.py	Utility script for generating scenario documentation GIFs
src/envs/doom_env/README.md	Extensive documentation (611 lines) with usage examples
docs/environments.md	Updated environment catalog with Doom card
.github/workflows/docker-build.yml	Added doom-env to CI/CD pipeline

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[AlirezaShamsoshoara]

Hey @init27, thanks for the review and spending time on it! I applied your comments and also other related comments here and resolved them.

[Darktex]

OMG this is amazing!