Ultimate TK Python Port for AI-GYM

Python port of Ultimate Tapan kaikki, with Gymnasium/PPO tooling for AI training and visible model playback.

Origin, Credits, License

• This project is based on the original repository: https://github.com/hkroger/ultimatetapankaikki.
• All credits for the original game, assets, and core design belong to the original author(s) and contributors.
• This repository follows GPL-3.0 licensing terms (see LICENSE).

Scope

• This is not a 100% finished port, differs many ways, for example enemy AI behaviour.
• Playable runtime is available (headless, terminal, pygame).
• AI modules are implemented for Gymnasium environment usage, PPO training/evaluation, and saved-model pygame playback.
• For transparency, development phase plans and progress notes are kept in-repo under docs/notes/ and python_refactor.md.

AI Interface

This project exposes the game as a Gymnasium environment for reinforcement learning experiments.

• Environment wrapper lives under src/ultimatetk/ai/.
• reset() starts a fresh headless gameplay episode (default flow starts from level 1) and returns the first observation.
• step(action) applies AI controls to the same core gameplay simulation used by the normal game runtime.
• Supported control dimensions include movement, turning, strafing, shooting, and weapon selection.

Observation Model

• The core spatial observation is a full 360° scan split into 32 equal angular segments around the player.
• Each segment encodes nearest directional context (for example obstacle/enemy/projectile presence and distance-style signals).
• Segment features are combined with player/runtime telemetry into a compact PPO-friendly state vector.

Rewards and Episode End

• Reward is shaped for learning-oriented behavior (survival, combat effectiveness, progression momentum).
• Reward shaping is still evolving and should be treated as an experiment surface, not a finalized benchmark setup.
• Episodes terminate on player death (death), successful run completion (game_completed), or configured step/time limits.

Important Expectations

• This repository does not ship guaranteed "win-the-game" hyperparameters.
• There are no official one-click training presets that reliably solve the game out of the box.
• The AI stack is a sandbox for experimentation, iteration, and learning-oriented RL workflows.
• PPO tools (tools/ppo_train.py, tools/ppo_eval.py, tools/ppo_play_pygame.py) are provided as practical baselines.

Requirements

• macOS/Linux/Windows with Conda installed (miniconda or anaconda)
• Python 3.12 (default target)

Conda Environment Setup

Option A: Recommended script setup

Creates or updates an environment and installs editable project dependencies (dev, pygame):

./scripts/setup_conda_env.sh

Custom environment name:

./scripts/setup_conda_env.sh my-env-name

Activate:

conda activate ultimatetk

Option B: Manual minimal runtime setup

conda create -y -n ultimatetk python=3.12 pip
conda activate ultimatetk
python -m pip install --upgrade pip
python -m pip install -e "."

Option C: Manual gameplay + pygame setup

conda create -y -n ultimatetk python=3.12 pip
conda activate ultimatetk
python -m pip install --upgrade pip
python -m pip install -e ".[dev,pygame]"

Option D: Full AI training setup (Gymnasium + PPO + TensorBoard)

Start from Option C (or script setup), then install AI dependencies in the same env:

conda install -y -n ultimatetk -c conda-forge numpy gymnasium pytorch stable-baselines3 tensorboard "setuptools<81"

Optional editable extras (in active env):

python -m pip install -e ".[ai]"
python -m pip install -e ".[ai_train]"

Run the Game

All commands assume repository root.

Headless

PYTHONPATH=src python3 -m ultimatetk --max-seconds 2 --autostart-gameplay --status-print-interval 40

Headless with scripted input replay

PYTHONPATH=src python3 -m ultimatetk --max-seconds 1.2 --autostart-gameplay --status-print-interval 20 --input-script "5:+MOVE_FORWARD;25:-MOVE_FORWARD;30:+TURN_LEFT;36:-TURN_LEFT"

Terminal backend

PYTHONPATH=src python3 -m ultimatetk --platform terminal --autostart-gameplay --status-print-interval 20

Pygame backend

PYTHONPATH=src python3 -m ultimatetk --platform pygame --autostart-gameplay --window-scale 3

Window scale examples:

• --window-scale 2 -> 640x400
• --window-scale 3 -> 960x600

AI / Gymnasium / PPO

Gym random-policy smoke test

python3 tools/gym_random_policy_smoke.py --episodes 1 --max-steps 300

Train PPO

python3 tools/ppo_train.py

Uses default training settings from tools/ppo_train.py.

PPO training parameters (tools/ppo_train.py)

Example:

python3 tools/ppo_train.py --device auto --total-timesteps 30000000 --batch-size 512

Common flags and defaults:

• --total-timesteps 5000000
• --n-envs 1
• --device auto
• --seed 123
• --n-steps 2048
• --batch-size 128
• --gamma 0.99
• --gae-lambda 0.95
• --clip-range 0.2
• --learning-rate-start 0.0003
• --learning-rate 0.00005
• --decay-ratio 0.8
• --ent-coef-start 0.05
• --ent-coef 0.01
• --max-episode-steps 6000
• --target-tick-rate 40
• --checkpoint-freq 1000000
• --eval-freq 25000
• --eval-episodes 5

Note:

• Run management flags: --run-name, --runs-root, --resume-from, --disable-asset-manifest-check, --render-training-scenes

Max-throughput training mode (uncapped training loop)

python3 tools/ppo_train.py --eval-freq 0 --checkpoint-freq 0

Resume training from checkpoint

python3 tools/ppo_train.py --resume-from runs/ai/ppo/<run>/checkpoints/ppo_model_50000_steps.zip

Evaluate a saved model

python3 tools/ppo_eval.py --model runs/ai/ppo/<run>/final_model.zip --episodes 5 --device auto

Play a saved AI model in pygame (normal FPS cap)

python3 tools/ppo_play_pygame.py --model runs/ai/ppo/<run>/final_model.zip --target-fps 40 --window-scale 3 --device auto

Useful playback flags:

• --max-seconds 30 limit playback wall time
• --max-steps 2000 limit simulation steps
• --allow-manual-input mix keyboard input with AI actions for debugging
• --stochastic enable sampling mode (default playback/eval is deterministic)

TensorBoard

Training writes logs under runs/ai/ppo/<run>/tensorboard.

tensorboard --logdir runs/ai/ppo/<run>/tensorboard --host 127.0.0.1 --port 6006

Open: http://127.0.0.1:6006/

Device notes

• Apple Silicon: --device auto defaults to CPU for throughput; use --device mps explicitly if needed.
• CUDA hosts: --device auto prefers CUDA when available; use --device cuda to force.
• CPU fallback: --device cpu.

Runtime Controls (Terminal + Pygame)

• Main menu: W/S or A/D select, Space/Enter/Tab confirm
• Movement/turn: WASD or arrow keys
• Strafe: Q / E
• Shoot: Space
• Next weapon: Tab (pygame also supports mouse wheel + PageUp/PageDown)
• Toggle shop: R or Enter
• Shop controls: W/S rows, A/D columns, Space buy, Tab sell
• Direct weapon slot: `, 1..0, - (pygame also supports numpad 0..9 and F1..F12)
• Quit: Esc

Verification

Default release verification:

python3 tools/release_verification.py

Strict legacy parity against archived root:

python3 tools/release_verification.py --legacy-compare-root /path/to/original/legacy-root

Project Paths

• Runtime assets: game_data/
• Runtime outputs and artifacts: runs/
• Phase notes: docs/notes/
• Refactor roadmap/progress log: python_refactor.md

Utility Commands

Regenerate asset manifest and gap report:

python3 tools/asset_manifest_report.py

Copy archived legacy assets into game_data/:

python3 tools/migrate_legacy_data.py --legacy-root /path/to/original/legacy-root

Probe format loaders:

python3 tools/format_probe.py

Render probe screenshot:

python3 tools/render_probe.py --output runs/screenshots/phase3_render_probe.ppm

AUTHOR

Timo Heimonen timo.heimonen@proton.me