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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 Files​

Core Game Logic (Headless-Compatible)​

  • constants.py - All game constants and configuration
  • core/__init__.py - Core module initialization
  • core/tile.py - Tile class with ownership and HP
  • core/unit.py - Unit class with all abilities
  • core/grid.py - Grid management with numpy conversion
  • core/game_state.py - Complete game state manager

Game Mechanics​

  • game/__init__.py - Game module initialization
  • game/mechanics.py - Combat, healing, structures, income
  • game/bot.py - SimpleBot AI for training
  • game/llm_bot.py - LLM-powered bots (OpenAI, Claude, Gemini)

UI Components​

  • ui/__init__.py - UI module initialization
  • ui/renderer.py - Pygame rendering system
  • ui/menus.py - All menu classes (MainMenu, GameModeMenu, MapSelectionMenu, PlayerConfigMenu, LoadGameMenu, SaveGameMenu, ReplaySelectionMenu, SettingsMenu, LanguageMenu, PauseMenu, GameOverMenu)

Reinforcement Learning​

  • rl/__init__.py - RL module initialization
  • rl/gym_env.py - Gymnasium environment wrapper
  • rl/action_space.py - Multi-discrete action encoding

Utilities​

  • utils/__init__.py - Utils module initialization
  • 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
  • utils/replay_player.py - Replay playback system

Training & Documentation​

  • main.py - Complete entry point with ~1000 lines including:
    • Training mode with PPO/A2C/DQN algorithms
    • Evaluation mode for testing trained agents
    • Interactive play mode with GUI
    • Stats viewing mode
    • Full CLI argument parsing
    • Bot integration (SimpleBot, LLM bots)
    • Save/load functionality
    • Replay playback
  • README.md - Comprehensive documentation
  • docs-site/ - Docusaurus documentation site deployed at reinforcetactics.com

⏳ TODO - Critical Files​

1. Game Controller​

File: game/controller.py

This would help organize game loop logic (though main.py handles most of this now):

"""
Game controller that bridges GameState and UI.
"""
from reinforcetactics.core.game_state import GameState
from reinforcetactics.ui.renderer import Renderer
from reinforcetactics.ui.menus import SaveGameMenu, PauseMenu
from reinforcetactics.game.bot import SimpleBot
import pygame

class GameController:
    """Manages game loop with rendering."""
    
    def __init__(self, map_file, bot_enabled=False):
        # Load map and create game state
        # Create renderer
        # Create bot if needed
        # Handle all event processing
        pass
    
    def run(self):
        """Main game loop with rendering."""
        pass

2. Advanced Bot AI​

Status: SimpleBot exists, but more sophisticated AI would improve training

Future improvements:

  • Normal difficulty bot
  • Hard difficulty bot with strategic planning
  • Minimax or MCTS-based bot

📁 Required Directories​

Create these directories for the game to work:

mkdir -p maps/1v1
mkdir -p saves
mkdir -p replays
mkdir -p models
mkdir -p checkpoints
mkdir -p logs
mkdir -p tensorboard
mkdir -p best_models

🗺️ Sample Map Files​

Create at least one test map in maps/1v1/test_map.csv:

p,p,p,p,p,p,p,p,p,p,p,p,p,p,p,p,p,p,p,p
p,p,p,p,p,p,p,p,p,p,p,p,p,p,p,p,p,p,p,p
p,p,b_1,p,p,p,p,p,p,p,p,p,p,p,p,p,b_2,p,p
p,p,p,h_1,p,p,p,p,p,p,p,p,p,p,p,h_2,p,p,p
p,p,b_1,p,p,p,p,p,p,p,p,p,p,p,p,p,b_2,p,p
p,p,p,p,p,p,p,p,p,p,p,p,p,p,p,p,p,p,p,p
p,p,p,p,p,p,p,p,t,p,p,t,p,p,p,p,p,p,p,p
p,p,p,p,p,p,p,p,p,p,p,p,p,p,p,p,p,p,p,p
p,p,p,p,p,p,p,p,p,p,p,p,p,p,p,p,p,p,p,p
p,p,p,p,p,p,p,p,p,p,p,p,p,p,p,p,p,p,p,p
p,p,p,p,p,p,p,p,p,p,p,p,p,p,p,p,p,p,p,p
p,p,p,p,p,p,p,p,p,p,p,p,p,p,p,p,p,p,p,p
p,p,p,p,p,p,p,p,t,p,p,t,p,p,p,p,p,p,p,p
p,p,p,p,p,p,p,p,p,p,p,p,p,p,p,p,p,p,p,p
p,p,b_2,p,p,p,p,p,p,p,p,p,p,p,p,p,b_1,p,p
p,p,p,h_2,p,p,p,p,p,p,p,p,p,p,p,h_1,p,p,p
p,p,b_2,p,p,p,p,p,p,p,p,p,p,p,p,p,b_1,p,p
p,p,p,p,p,p,p,p,p,p,p,p,p,p,p,p,p,p,p,p
p,p,p,p,p,p,p,p,p,p,p,p,p,p,p,p,p,p,p,p
p,p,p,p,p,p,p,p,p,p,p,p,p,p,p,p,p,p,p,p

🚀 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.generate_random_map(20, 20, num_players=2)
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(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

📝 Implementation Notes​

What Works Now​

  1. Full GUI gameplay - Complete game with menus, rendering, and all features
  2. Headless mode - Full game logic without rendering for fast training
  3. RL training - Train agents using Stable-Baselines3 (PPO, A2C, DQN)
  4. Bot opponents - SimpleBot and LLM-powered bots (GPT, Claude, Gemini)
  5. All game mechanics - Combat, structures, economy, status effects
  6. Action encoding - Multi-discrete action space for RL
  7. File I/O - Save/load games, map loading, random generation
  8. Menu system - Comprehensive menu system with all game modes
  9. Save/Load system - Full game state serialization and persistence
  10. Replay system - Record and playback games with video export
  11. Multi-language support - Language selection and localization (English, French, Korean, Spanish, Chinese)
  12. Settings management - API keys, preferences, and configuration
  13. Docker support - Containerized deployment
  14. Documentation site - Deployed at reinforcetactics.com

All menu classes are self-contained and can be used without manually creating a pygame screen:

from reinforcetactics.ui.menus import MainMenu, MapSelectionMenu, LoadGameMenu

# Menus create their own screen if needed
main_menu = MainMenu()  # No screen parameter required
result = main_menu.run()  # Returns dict with user's choice

# MainMenu handles internal navigation automatically
if result['type'] == 'new_game':
    # Result includes: {'type': 'new_game', 'map': 'path/to/map.csv', 'mode': '1v1' or '2v2'}
    start_game(result['map'], result['mode'])
elif result['type'] == 'load_game':
    # LoadGameMenu returns dict with save data already loaded
    save_data = result.get('save_data')
elif result['type'] == 'watch_replay':
    # Result includes: {'type': 'watch_replay', 'replay_path': 'path/to/replay.json'}
    watch_replay(result['replay_path'])

Available Menu Classes:

  • MainMenu() - Main game menu with navigation to sub-menus
  • GameModeMenu(screen, maps_dir) - Select game mode (1v1 or 2v2)
  • MapSelectionMenu(screen, maps_dir, game_mode) - Select map for new game
  • PlayerConfigMenu(screen, game_mode) - Configure players as human or computer
  • LoadGameMenu() - Load saved game (returns loaded dict)
  • SaveGameMenu(game) - Save current game
  • ReplaySelectionMenu() - Select replay to watch
  • PauseMenu() - In-game pause menu
  • SettingsMenu() - Game settings
  • LanguageMenu() - Language selection
  • GameOverMenu(winner, game_state) - Game over screen

New Game Flow: The "New Game" menu now uses a three-step selection process:

  1. Game Mode Selection - User chooses between "1v1" or "2v2" (dynamically discovered from maps/ folder structure)
  2. Map Selection - User selects a map from the chosen game mode folder (displays only relevant maps)
  3. Player Configuration - User configures each player as human or computer (with bot difficulty selection for computer players)

What's Missing​

  1. Advanced bot AI - Normal and hard difficulty bots with strategic planning
  2. Complete action masking - Proper action filtering for more efficient RL training
  3. Map editor GUI - In-game map creation and editing tools
  4. Multiplayer (3-4 players) - Support for more than 2 players
  5. Tournament/ladder system - Competitive ranking and matchmaking
  6. Better graphics/animations - Enhanced visual effects and unit animations
  7. Sound effects and music - Audio feedback for actions and events

Implementation Priority​

  1. High Priority (for enhanced gameplay)

    • Advanced bot AI (Normal/Hard difficulty)
    • Complete action masking for RL efficiency
    • Better graphics and animations

  2. Medium Priority (for additional features)

    • Map editor GUI
    • Multiplayer support (3-4 players)
    • Sound effects and music
    • Tournament/ladder system

  3. Low Priority (nice to have)

    • Additional unit types and abilities
    • Advanced terrain effects
    • Fog of war
    • Campaign mode with story

🎯 Next Steps​

  1. Enhance bot AI - Implement Normal and Hard difficulty levels
  2. Improve action masking - Complete implementation for efficient RL training
  3. Add graphics polish - Better animations and visual effects
  4. Create map editor - GUI tool for creating custom maps
  5. Test extensively - Comprehensive testing of all game modes
  6. Performance optimization - Profile and optimize bottlenecks

💡 Tips for Implementation​

  • Start simple: Get basic human vs human working first
  • Test incrementally: Test each component as you build it
  • Use headless mode: Much faster for testing game logic
  • Leverage existing code: Most of the hard work is done!
  • Focus on controller: The GameController is the key missing piece

🐛 Known Issues to Address​

  1. Action masking: Currently returns all 1s, need proper implementation
  2. Invalid actions: Agent will try invalid actions and get penalties
  3. Performance: Large action space may be slow, consider action filtering
  4. Video recording: Requires opencv-python (optional)

✨ What's Great About This Structure​

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

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