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Implementation Status

This page tracks the current implementation status of the Reinforce Tactics project, including completed features, pending tasks, and implementation priorities.

✅ Completed Features​

Core Game Logic (Headless-Compatible)​

  • reinforcetactics/constants.py - All game constants and configuration (8 unit types, terrain, structures)
  • reinforcetactics/core/tile.py - Tile class with ownership and HP
  • reinforcetactics/core/unit.py - Unit class with all 8 unit types and abilities
  • reinforcetactics/core/grid.py - Grid management with numpy conversion
  • reinforcetactics/core/game_state.py - Complete game state manager
  • reinforcetactics/core/visibility.py - Fog of war visibility system

Game Mechanics​

  • game/mechanics.py - Combat, healing, structures, income, all special abilities
  • game/bot.py - SimpleBot, MediumBot, and AdvancedBot AI for training
  • game/llm_bot.py - LLM-powered bots (OpenAI GPT, Claude, Gemini)
  • game/model_bot.py - Trained model bot for tournament play

UI Components​

  • ui/renderer.py - Pygame rendering system with sprite animations
  • ui/icons.py - Unit and terrain icons
  • ui/menus/ - Complete menu system (30 files across 5 subdirectories)
  • ui/menus/map_editor/ - Full map editor GUI

Reinforcement Learning​

  • rl/gym_env.py - Full Gymnasium environment wrapper with 10 action types
  • rl/masking.py - Action masking for valid moves
  • rl/self_play.py - Self-play training with opponent pool
  • rl/feudal_rl.py - Hierarchical RL (Manager-Worker) architecture with full training loop

Tournament System​

  • tournament/runner.py - Tournament execution engine
  • tournament/elo.py - ELO rating system
  • tournament/bots.py - Bot descriptors and factory
  • tournament/schedule.py - Round-robin scheduling with resume support
  • tournament/results.py - Results tracking and export
  • tournament/config.py - Tournament configuration

Utilities​

  • utils/file_io.py - File I/O for maps, saves, replays
  • utils/settings.py - Settings management with API keys
  • utils/language.py - Multi-language support (English, Korean, Spanish, French, Chinese)
  • utils/replay_player.py - Replay playback system with video export
  • utils/experiment_tracker.py - RL experiment logging

Training & Documentation​

  • main.py - Complete CLI entry point with train/evaluate/play modes
  • train/train_self_play.py - Self-play training with opponent pool
  • train/train_feudal_rl.py - Feudal RL training
  • README.md - Comprehensive documentation
  • docs-site/ - Docusaurus documentation site deployed at reinforcetactics.com
  • Docker support for containerized deployment

📊 Feature Summary​

CategoryFeaturesStatus
Unit Types8 (Warrior, Mage, Cleric, Archer, Knight, Rogue, Sorcerer, Barbarian)✅ Complete
Special AbilitiesParalyze, Heal, Cure, Charge, Flank, Evade, Haste, Attack Buff, Defence Buff✅ Complete
Terrain Types6 (Grass, Water, Ocean, Mountain, Forest, Road)✅ Complete
Structure Types3 (HQ, Building, Tower)✅ Complete
Bot TypesSimpleBot, MediumBot, AdvancedBot, LLM Bots (3 providers), ModelBot✅ Complete
RL FeaturesGymnasium env, Action masking, Self-play, Feudal HRL✅ Complete
TournamentRound-robin, ELO ratings, Resume, Multi-map✅ Complete
Fog of WarFull visibility system with explored/visible states✅ Complete
Map EditorGUI-based map creation and editing✅ Complete
Replay SystemRecord, playback, video export✅ Complete
Localization5 languages✅ Complete

🚀 Quick Start Testing​

Test Headless Mode (No GUI)​

# test_headless.py
from reinforcetactics.core.game_state import GameState
from reinforcetactics.utils.file_io import FileIO

map_data = FileIO.load_map('maps/1v1/beginner.csv')
game = GameState(map_data)

# Create some units
game.create_unit('W', 5, 5, player=1)
game.create_unit('M', 6, 5, player=1)

print(f"Player 1 units: {len([u for u in game.units if u.player == 1])}")
print(f"Player 1 gold: ${game.player_gold[1]}")

# End turn
game.end_turn()
print(f"Player 2 turn started")
print("Headless mode working!")

Test RL Environment​

# test_rl.py
from reinforcetactics.rl.gym_env import StrategyGameEnv

env = StrategyGameEnv(map_file='maps/1v1/beginner.csv', opponent='bot')
obs, info = env.reset()

print("Observation space:", env.observation_space)
print("Action space:", env.action_space)
print("Grid shape:", obs['grid'].shape)
print("RL environment working!")

# Take a few random actions
for _ in range(5):
    action = env.action_space.sample()
    obs, reward, terminated, truncated, info = env.step(action)
    if terminated:
        break

print("Episode completed successfully!")

Test Training (Quick)​

python main.py --mode train --algorithm ppo --timesteps 1000

Test GUI Mode​

python main.py --mode play

📝 Action Space Reference​

The Gymnasium environment uses a MultiDiscrete action space with 6 dimensions:

DimensionDescriptionValues
action_typeType of action0=create, 1=move, 2=attack, 3=seize, 4=heal, 5=end_turn, 6=paralyze, 7=haste, 8=defence_buff, 9=attack_buff
unit_typeUnit type for creation0=W, 1=M, 2=C, 3=A, 4=K, 5=R, 6=S, 7=B
from_xSource X coordinate0 to grid_width-1
from_ySource Y coordinate0 to grid_height-1
to_xTarget X coordinate0 to grid_width-1
to_yTarget Y coordinate0 to grid_height-1

🔮 Future Enhancements​

High Priority​

  • Sound effects and music
  • Better graphics and animations
  • Additional maps

Medium Priority​

  • Campaign mode with story
  • Online multiplayer
  • More advanced bot strategies (MCTS, minimax)

Low Priority​

  • Additional unit types
  • Advanced terrain effects
  • Seasonal events

✨ Architecture Highlights​

  • Fully modular - Each component is independent
  • Headless-compatible - Train without rendering overhead
  • RL-ready - Standard Gymnasium interface with action masking
  • Extensible - Easy to add new units, mechanics, rewards
  • Well-documented - Comprehensive README and docstrings
  • Production-ready - Proper package structure with pip install support
  • Full-featured - Complete game with GUI, save/load, replays
  • LLM integration - Support for GPT, Claude, and Gemini bots
  • Docker support - Easy deployment and development
  • Tournament system - ELO-rated competitive play

The project is feature-complete and ready for gameplay, training, and further enhancement!