Trianglengin

MANIFEST.in

@@ -1,4 +1,6 @@
 # File: MANIFEST.in
+# File: alphatriangle/MANIFEST.in
+# File: MANIFEST.in
 include README.md
 include LICENSE
 include requirements.txt
@@ -7,4 +9,20 @@ graft tests
 include .python-version
 include pyproject.toml
 global-exclude __pycache__
-global-exclude *.py[co]
+global-exclude *.py[co]
+# Remove pruned directories
+prune alphatriangle/visualization
+prune alphatriangle/interaction
+# REMOVE environment and structs pruning (they are part of trianglengin now)
+# prune alphatriangle/environment
+# prune alphatriangle/structs
+# Remove pruned files
+global-exclude alphatriangle/app.py
+# Remove pruned test directories
+prune tests/visualization
+prune tests/interaction
+# REMOVE environment and structs test pruning
+# prune tests/environment
+# prune tests/structs
+# Remove pruned core files
+global-exclude alphatriangle/rl/core/visual_state_actor.py

alphatriangle/cli.py

@@ -1,29 +1,22 @@
-# File: alphatriangle/cli.py
 import logging
 import sys
 from typing import Annotated
 
 import typer
 
-from alphatriangle import config, utils
-from alphatriangle.app import Application
+# Import EnvConfig from trianglengin
+# Import alphatriangle specific configs and runner
 from alphatriangle.config import (
-    MCTSConfig,
     PersistenceConfig,
     TrainConfig,
 )
 
-# --- REVERTED: Import from the re-exporting runners.py ---
-from alphatriangle.training.runners import (
-    run_training_headless_mode,
-    run_training_visual_mode,
-)
-
-# --- END REVERTED ---
+# Import the single runner function
+from alphatriangle.training.runners import run_training
 
 app = typer.Typer(
     name="alphatriangle",
-    help="AlphaZero implementation for a triangle puzzle game.",
+    help="AlphaZero training pipeline for a triangle puzzle game (uses trianglengin).",
     add_completion=False,
 )
 
@@ -64,87 +57,39 @@ def setup_logging(log_level_str: str):
         handlers=[logging.StreamHandler(sys.stdout)],
         force=True,  # Override existing config
     )
-    logging.getLogger("ray").setLevel(logging.WARNING)  # Keep Ray less verbose
-    logging.getLogger("matplotlib").setLevel(
-        logging.WARNING
-    )  # Keep Matplotlib less verbose
+    logging.getLogger("ray").setLevel(logging.WARNING)
+    # Add trianglengin logger control if needed
+    logging.getLogger("trianglengin").setLevel(
+        logging.INFO
+    )  # Example: Set engine log level
     logging.info(f"Root logger level set to {logging.getLevelName(log_level)}")
 
 
-def run_interactive_mode(mode: str, seed: int, log_level: str):
-    """Runs the interactive application."""
-    setup_logging(log_level)
-    logger = logging.getLogger(__name__)  # Get logger after setup
-    logger.info(f"Running in {mode.capitalize()} mode...")
-    utils.set_random_seeds(seed)
-
-    mcts_config = MCTSConfig()
-    config.print_config_info_and_validate(mcts_config)
-
-    try:
-        app_instance = Application(mode=mode)
-        app_instance.run()
-    except ImportError as e:
-        logger.error(f"Runtime ImportError: {e}")
-        logger.error("Please ensure all dependencies are installed.")
-        sys.exit(1)
-    except Exception as e:
-        logger.critical(f"An unhandled error occurred: {e}", exc_info=True)
-        sys.exit(1)
-
-    logger.info("Exiting.")
-    sys.exit(0)
-
-
-@app.command()
-def play(
-    log_level: LogLevelOption = "INFO",
-    seed: SeedOption = 42,
-):
-    """Run the game in interactive Play mode."""
-    run_interactive_mode(mode="play", seed=seed, log_level=log_level)
-
-
-@app.command()
-def debug(
-    log_level: LogLevelOption = "INFO",
-    seed: SeedOption = 42,
-):
-    """Run the game in interactive Debug mode."""
-    run_interactive_mode(mode="debug", seed=seed, log_level=log_level)
+# --- REMOVED run_interactive_mode, play, debug commands ---
 
 
 @app.command()
 def train(
-    headless: Annotated[
-        bool,
-        typer.Option("--headless", "-H", help="Run training without visualization."),
-    ] = False,
+    # REMOVE headless option - it's always headless now
     log_level: LogLevelOption = "INFO",
     seed: SeedOption = 42,
 ):
-    """Run the AlphaTriangle training pipeline."""
+    """Run the AlphaTriangle training pipeline (headless)."""
     setup_logging(log_level)
     logger = logging.getLogger(__name__)
 
+    # Use alphatriangle configs here
     train_config_override = TrainConfig()
     persist_config_override = PersistenceConfig()
     train_config_override.RANDOM_SEED = seed
 
-    if headless:
-        logger.info("Starting training in Headless mode...")
-        exit_code = run_training_headless_mode(
-            log_level_str=log_level,
-            train_config_override=train_config_override,
-            persist_config_override=persist_config_override,
-        )
-    else:
-        logger.info("Starting training in Visual mode...")
-        exit_code = run_training_visual_mode(
-            log_level_str=log_level,
-            train_config_override=train_config_override,
-            persist_config_override=persist_config_override,
-        )
+    logger.info("Starting training...")
+    # Call the single runner function directly
+    exit_code = run_training(
+        log_level_str=log_level,
+        train_config_override=train_config_override,
+        persist_config_override=persist_config_override,
+    )
 
     logger.info(f"Training finished with exit code {exit_code}.")
     sys.exit(exit_code)

alphatriangle/config/README.md

@@ -1,9 +1,9 @@
-# File: alphatriangle/config/README.md
+
 # Configuration Module (`alphatriangle.config`)
 
 ## Purpose and Architecture
 
-This module centralizes all configuration parameters for the AlphaTriangle project. It uses separate **Pydantic models** for different aspects of the application (environment, model, training, visualization, persistence) to promote modularity, clarity, and automatic validation.
+This module centralizes all configuration parameters for the AlphaTriangle project. It uses separate **Pydantic models** for different aspects of the application (environment, model, training, persistence, MCTS) to promote modularity, clarity, and automatic validation.
 
 -   **Modularity:** Separating configurations makes it easier to manage parameters for different components.
 -   **Type Safety & Validation:** Using Pydantic models (`BaseModel`) provides strong type hinting, automatic parsing, and validation of configuration values based on defined types and constraints (e.g., `Field(gt=0)`).
@@ -15,10 +15,9 @@ This module centralizes all configuration parameters for the AlphaTriangle proje
 ## Exposed Interfaces
 
 -   **Pydantic Models:**
-    -   [`EnvConfig`](env_config.py): Environment parameters (grid size, shapes).
+    -   [`EnvConfig`](env_config.py): Environment parameters (grid size, shapes) - **Imported from `trianglengin`**.
     -   [`ModelConfig`](model_config.py): Neural network architecture parameters. **Defaults tuned for larger capacity.**
     -   [`TrainConfig`](train_config.py): Training loop hyperparameters (batch size, learning rate, workers, **PER settings**, etc.). **Defaults tuned for longer runs.**
-    -   [`VisConfig`](vis_config.py): Visualization parameters (screen size, FPS, layout).
     -   [`PersistenceConfig`](persistence_config.py): Data saving/loading parameters (directories, filenames).
     -   [`MCTSConfig`](mcts_config.py): MCTS parameters (simulations, exploration constants, temperature).
 -   **Constants:**
@@ -31,6 +30,7 @@ This module centralizes all configuration parameters for the AlphaTriangle proje
 This module primarily defines configurations and relies heavily on **Pydantic**.
 
 -   **`pydantic`**: The core library used for defining models and validation.
+-   **`trianglengin.config`**: Imports `EnvConfig`.
 -   **Standard Libraries:** `typing`, `time`, `os`, `logging`, `pathlib`.
 
 ---

alphatriangle/config/__init__.py

@@ -1,19 +1,21 @@
+from trianglengin.config import EnvConfig
+
 from .app_config import APP_NAME
-from .env_config import EnvConfig
 from .mcts_config import MCTSConfig
 from .model_config import ModelConfig
 from .persistence_config import PersistenceConfig
 from .train_config import TrainConfig
 from .validation import print_config_info_and_validate
-from .vis_config import VisConfig
+
+# REMOVE DisplayConfig import
 
 __all__ = [
     "APP_NAME",
-    "EnvConfig",
+    "EnvConfig",  # Now imported from trianglengin
     "ModelConfig",
     "PersistenceConfig",
     "TrainConfig",
-    "VisConfig",
+    # "DisplayConfig", # REMOVED
     "MCTSConfig",
     "print_config_info_and_validate",
 ]

alphatriangle/config/persistence_config.py

@@ -1,3 +1,4 @@
+# File: alphatriangle/config/persistence_config.py
 from pathlib import Path
 
 from pydantic import BaseModel, Field, computed_field
@@ -12,8 +13,9 @@ class PersistenceConfig(BaseModel):
 
     CHECKPOINT_SAVE_DIR_NAME: str = Field(default="checkpoints")
     BUFFER_SAVE_DIR_NAME: str = Field(default="buffers")
-    GAME_STATE_SAVE_DIR_NAME: str = Field(default="game_states")
+    # REMOVED GAME_STATE_SAVE_DIR_NAME (handled externally now)
     LOG_DIR_NAME: str = Field(default="logs")
+    TENSORBOARD_DIR_NAME: str = Field(default="tensorboard")  # ADDED
 
     LATEST_CHECKPOINT_FILENAME: str = Field(default="latest.pkl")
     BEST_CHECKPOINT_FILENAME: str = Field(default="best.pkl")
@@ -22,11 +24,10 @@ class PersistenceConfig(BaseModel):
 
     RUN_NAME: str = Field(default="default_run")
 
-    SAVE_GAME_STATES: bool = Field(default=False)
-    GAME_STATE_SAVE_FREQ_EPISODES: int = Field(default=5, ge=1)
+    # REMOVED SAVE_GAME_STATES and related freq
 
     SAVE_BUFFER: bool = Field(default=True)
-    BUFFER_SAVE_FREQ_STEPS: int = Field(default=10, ge=1)
+    BUFFER_SAVE_FREQ_STEPS: int = Field(default=1000, ge=1)  # Increased default freq
 
     @computed_field  # type: ignore[misc] # Decorator requires Pydantic v2
     @property
@@ -54,6 +55,13 @@ def get_mlflow_abs_path(self) -> str:
         abs_path = Path(self.ROOT_DATA_DIR).joinpath(self.MLFLOW_DIR_NAME).resolve()
         return str(abs_path)
 
+    def get_tensorboard_log_dir(self, run_name: str | None = None) -> str:
+        """Gets the directory for TensorBoard logs for a specific run."""
+        run_base = self.get_run_base_dir(run_name)
+        if not run_base or not hasattr(self, "TENSORBOARD_DIR_NAME"):
+            return ""  # Fallback
+        return str(Path(run_base) / self.TENSORBOARD_DIR_NAME)
+
 
 # Ensure model is rebuilt after changes
 PersistenceConfig.model_rebuild(force=True)

alphatriangle/config/train_config.py

@@ -1,4 +1,5 @@
 # File: alphatriangle/config/train_config.py
+# File: alphatriangle/config/train_config.py
 import logging
 import time
 from typing import Literal