411 lines
20 KiB
Python
411 lines
20 KiB
Python
"""
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AI HTTP server interface for AI training
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Opens a TCP socket to listen for incoming connections from an AI client.
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When a new connection is established, the server intialise a new training session :
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- Model Score is set to 0
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For the duration of the training session, two mapSets are created, one with hard maps and one with all of the other maps.
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When the AI client sends a request for a new game or validation game,
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the server loads a map based on the saved best score or from the hard mapSet if a validation game is requested,
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and sends it to the client as a matrix of block types strings.
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The client then sends back a list of moves to be executed by the game engine. E
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Each item of the list is a different model givin a string representing a move from GameState.PLAYER_MOVEMENTS to execute in the simulation.
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The server then executes the moves in the game engine for each model
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and sends back the new player position or game scores
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(calculated here using formula with weights) if the game is finished to the client.
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This process is repeated until all models have finished the game.
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The client will then train the model based on their scores
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and the server set the Model Score to the best score of the models
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and shows in a separate thread the best model series of moves on the previous map.
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The client then sends a new request for a new game and the process repeats.
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The simulation runs in a pool of threads to allow multiple models to be trained in parallel.
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The handler makes sure an AI client is not stuck and only allows a maximum number
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of moves to be executed in a single game simulation, based on the map min_moves value.
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"""
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import json
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import math
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import os
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import random
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import socketserver
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from concurrent.futures import ThreadPoolExecutor
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from copy import deepcopy
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from threading import Condition, Thread
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from time import sleep
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from game.handlers.aiSimulation import AiSimulation
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from game.game import Game
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from game.gameState import GameState
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from game.handlers.gameHandler import GameHandler
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from game.map import Map
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from game.mapReader import MapReader
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from game.mapSet import MapSet
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class AiHttpGameHandler(GameHandler):
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DEFAULT_HOST = "127.0.0.1"
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DEFAULT_PORT = 8765
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VALIDATION_DIFFICULTY = 90
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REPLAY_FRAME_DELAY = 0.1
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# Scoring weights TOTAL MUST BE 100
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COINS_WEIGHT = 35
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STEPS_WEIGHT = 10
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DISTANCE_WEIGHT = 10
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WIN_WEIGHT = 45
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PLAY_ALL_MAPS_PER_GENERATION = True # Are all maps played during a generation or only MAPS_PER_GENERATION maps per generation are played
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MAPS_PER_GENERATION = 9 # Evaluate across x maps per generation. Only used if PLAY_ALL_MAPS_PER_GENERATION is False. If True, all maps are played in a generation.
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CONSISTENCY_PENALTY_WEIGHT = 0.4 # Penalty for inconsistent map scores
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def __init__(self, host: str | None = None, port: int | None = None,
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max_workers: int | None = None):
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super().__init__()
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self.host = host or os.getenv("AI_HOST", self.DEFAULT_HOST)
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self.port = port or int(os.getenv("AI_PORT", self.DEFAULT_PORT))
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self.max_workers = max_workers or int(os.getenv("AI_MAX_WORKERS", "4"))
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self.map_reader = MapReader()
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self.map_set = MapSet(self.map_reader, self.map_reader.get_map_names())
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self.validation_maps = [
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game_map for game_map in self.map_set.maps
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if game_map.difficulty >= self.VALIDATION_DIFFICULTY
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] or list(self.map_set.maps)
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if (self.COINS_WEIGHT + self.DISTANCE_WEIGHT + self.STEPS_WEIGHT + self.WIN_WEIGHT) != 100:
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raise ValueError("Scoring weights must sum to 100.")
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def run(self) -> None:
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handler = self
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class RequestHandler(socketserver.StreamRequestHandler):
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def handle(self) -> None:
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session = {
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"performance": 0.0,
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"simulations": None,
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"best_run": None,
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"replay_condition": Condition(),
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"client_connected": True,
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"stagnant_generations": 0,
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"map_round": 0,
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"cumulative_scores": {},
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"last_results": {}
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}
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replay_thread = Thread(target=handler._replay_best_runs, args=(session,))
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replay_thread.start()
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print(f"AI client connected: {self.client_address}", flush=True)
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try:
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for raw_request in self.rfile:
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try:
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request = json.loads(raw_request)
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response = handler._handle_request(request, session)
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except (json.JSONDecodeError, TypeError, ValueError, KeyError) as error:
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response = {"ok": False, "type": "error", "error": str(error)}
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compact_response = json.dumps(response, separators=(",", ":"))
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self.wfile.write((compact_response + "\n").encode("utf-8"))
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self.wfile.flush()
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finally:
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with session["replay_condition"]:
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session["client_connected"] = False
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session["replay_condition"].notify_all()
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replay_thread.join()
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print(f"AI client disconnected: {self.client_address}", flush=True)
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class ThreadingTCPServer(socketserver.ThreadingTCPServer):
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allow_reuse_address = True
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with ThreadingTCPServer((self.host, self.port), RequestHandler) as server:
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self.port = server.server_address[1]
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print(f"AI TCP server listening on {self.host}:{self.port}")
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server.serve_forever()
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def _handle_request(self, request: dict, session: dict | None = None) -> dict:
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if not isinstance(request, dict):
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raise ValueError("Each request must be a JSON object.")
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request_type = request.get("type")
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if request_type == "ping":
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return {"ok": True, "type": "pong"}
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if request_type == "simulate":
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return self._simulate(request, session)
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if request_type == "step":
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return self._step(request, session)
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raise ValueError("Unknown request type. Expected 'ping', 'simulate', or 'step'.")
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def _simulate(self, request: dict, session: dict | None = None) -> dict:
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models = request.get("models")
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if not isinstance(models, list) or not models:
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raise ValueError("'models' must be a non-empty list.")
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if not all(isinstance(model, dict) for model in models):
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raise ValueError("Each item in 'models' must be a JSON object.")
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validation = bool(request.get("validation", False))
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if session is None:
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raise ValueError("Simulation requests must belong to a TCP session.")
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if session.get("simulations"):
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raise ValueError("A simulation is already active in this session.")
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# Initialize generation round tracking and select 3 maps upfront if starting fresh
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if session.get("map_round", 0) == 0:
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session["generation"] = session.get("generation", 0) + 1
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session["map_scores"] = {model.get("id"): [] for model in models}
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# Pre-select the 3 maps for this entire generation based on current performance
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if validation:
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session["generation_maps"] = [
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random.choice(self.validation_maps)
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for _ in range(self.MAPS_PER_GENERATION)
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]
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else:
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perf = session.get("performance", 0.0)
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if self.PLAY_ALL_MAPS_PER_GENERATION:
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session["generation_maps"] = self.map_set.get_maps()
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else:
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available_maps = self.map_set.get_maps_by_difficulty(round(perf), self.MAPS_PER_GENERATION)
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session["generation_maps"] = [
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random.choice(available_maps)
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for _ in range(self.MAPS_PER_GENERATION) # Randomly selects in case there is less maps available than MAPS_PER_GENERATION
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]
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# Fetch the pre-selected map for the current round
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map_round = session.get("map_round", 0)
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selected_map = session["generation_maps"][map_round]
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session["last_result"] = None
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simulations = {}
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for model in models:
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model_id = model.get("id")
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if not isinstance(model_id, str) or not model_id:
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raise ValueError("Each model must have a non-empty string 'id'.")
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if model_id in simulations:
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raise ValueError(f"Duplicate model id '{model_id}'.")
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simulation = AiSimulation(selected_map)
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simulation.start()
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simulations[model_id] = simulation
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session["simulations"] = simulations
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session["map"] = selected_map
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return {
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"ok": True,
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"type": "move_request",
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"map": self._map_payload(selected_map),
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"results": self._results(simulations),
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}
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def _step(self, request: dict, session: dict | None) -> dict:
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if session is None:
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raise ValueError("No simulation is active. Send a 'simulate' request first.")
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if not session.get("simulations"):
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if session.get("last_result") is not None:
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return session["last_result"]
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raise ValueError("No simulation is active. Send a 'simulate' request first.")
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moves = request.get("moves")
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# print(
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# "Step payload contains moves for: "
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# f"{list(moves) if isinstance(moves, dict) else type(moves).__name__}",
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# flush=True,
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# )
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if not isinstance(moves, dict):
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raise ValueError("'moves' must be an object mapping model ids to moves.")
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simulations = session["simulations"]
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if set(moves) != set(simulations):
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raise ValueError("'moves' must contain exactly one move for every model.")
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for model_id, move in moves.items():
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if move is not None and (not isinstance(move, str) or move not in GameState.PLAYER_MOVEMENTS):
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raise ValueError(f"Invalid move for model '{model_id}'.")
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# print("Dispatching step to simulation workers", flush=True)
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def step_model(item):
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model_id, move = item
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simulation = simulations[model_id]
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# print(f"Starting step for model '{model_id}'", flush=True)
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result = simulation.step(move)
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result["score"] = self._score_simulation(simulation, result)
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# print(f"Finished step for model '{model_id}'", flush=True)
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return model_id, result
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with ThreadPoolExecutor(max_workers=min(self.max_workers, len(simulations))) as executor:
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results = dict(executor.map(step_model, moves.items()))
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# print(f"Completed step for models: {list(moves)}", flush=True)
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all_finished = all(result["finished"] for result in results.values())
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response = {"ok": True, "type": "move_request", "results": results}
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if all_finished:
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session["map_round"] += 1
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# Accumulate scores for this map round
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for model_id, res in results.items():
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session["map_scores"][model_id].append(res["score"])
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# Check if we have completed all maps for this generation
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if session["map_round"] < self.MAPS_PER_JSON_ROUND if hasattr(self, 'MAPS_PER_JSON_ROUND') else session["map_round"] < self.MAPS_PER_GENERATION:
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# More maps remaining in this generation! Tell client to simulate next map.
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response["type"] = "map_round_complete"
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response["map_round"] = session["map_round"]
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session["simulations"] = None
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session["last_result"] = response
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else:
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# All 3 maps completed -> finalize generation results (Average scores)
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response["type"] = "simulation_result"
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final_results = {}
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# Calculate final scores for each model based on their score on every map
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for model_id, scores in session["map_scores"].items():
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# Calculate Mean Squared Error from perfection (100)
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squared_errors = [(100.0 - score) ** 2 for score in scores]
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mse = sum(squared_errors) / len(squared_errors)
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rmse = math.sqrt(mse)
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# Final score penalizes variance/drops sharply based on RMSE
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final_score = round(max(0.0, min(100.0, 100.0 - rmse)), 2)
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# Attach final structural properties from the last results set
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final_results[model_id] = deepcopy(results[model_id])
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final_results[model_id]["score"] = final_score
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response["results"] = final_results
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best_score = max(res["score"] for res in final_results.values())
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best_model_id = max(final_results, key=lambda mid: final_results[mid]["score"])
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map_difficulty = session["map"].difficulty
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response["score"] = best_score
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# --- Curriculum Regression & Performance Update ---
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PASSING_THRESHOLD = 85.0
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if best_score < PASSING_THRESHOLD:
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session["stagnant_generations"] = session.get("stagnant_generations", 0) + 1
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else:
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session["stagnant_generations"] = 0
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if session["stagnant_generations"] >= 15:
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current_perf = session.get("performance", 10 * math.sqrt(map_difficulty))
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regression_drop = 15.0
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session["performance"] = max(0.0, current_perf - regression_drop)
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print(f"Stuck on difficulty {map_difficulty} for {session['stagnant_generations']} generations. Dropping target performance to {session['performance']:.1f}", flush=True)
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session["stagnant_generations"] = 0
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else:
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difficulty_scaling = 10 * math.sqrt(map_difficulty)
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normalized_score = best_score / 100.0
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session["performance"] = difficulty_scaling * (0.5 + 0.5 * normalized_score)
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# Reset round counter for the next generation
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session["map_round"] = 0
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session["last_result"] = response
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session["simulations"] = None
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session["best_run"] = (
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deepcopy(session["map"]),
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list(simulations[best_model_id].moves),
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session["generation"],
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)
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replay_condition = session.get("replay_condition")
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if replay_condition is not None:
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with replay_condition:
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replay_condition.notify_all()
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return response
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def _replay_best_runs(self, session: dict) -> None:
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condition = session["replay_condition"]
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while True:
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with condition:
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condition.wait_for(
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lambda: session["best_run"] is not None
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or not session["client_connected"]
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)
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best_run = session["best_run"]
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session["best_run"] = None
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if best_run is None:
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return
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self._show_best_run(*best_run)
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def _show_best_run(self, game_map, moves: list[str], generation: int) -> None:
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game = Game(game_map, GameState())
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remaining_moves = iter(moves)
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def on_input_request() -> str:
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try:
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return next(remaining_moves)
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except StopIteration:
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game.stop_game()
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return -1
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def on_position_update() -> None:
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game_string = game.to_string()
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frame_lines = []
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for index, line in enumerate(game_string.splitlines()):
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if index == 0:
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line += "\tCoins: " + str(game.gameState.coins)
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elif index == 1:
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line += "\tMoves: " + str(game.gameState.movesCount)
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elif index == 2:
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line += "\tGeneration: " + str(generation)
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elif index == 3:
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line += "\tMove: " + moves[game.gameState.movesCount - 1] if len(moves) >= game.gameState.movesCount else "\tMove: None"
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frame_lines.append(line)
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print("\033[2J\033[H\n\n\n" + "\n".join(frame_lines), flush=True)
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sleep(self.REPLAY_FRAME_DELAY)
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game.set_waiting_for_input_callback(on_input_request)
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game.set_position_update_callback(on_position_update)
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game.start_game()
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def _results(self, simulations: dict) -> dict:
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results = {}
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for model_id, simulation in simulations.items():
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result = simulation.result()
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result["score"] = self._score_simulation(simulation, result)
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results[model_id] = result
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return results
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def _select_map(self, performance: float, validation: bool):
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if validation:
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return random.choice(self.validation_maps)
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maps = self.map_set.get_maps_by_difficulty(round(performance), 2)
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return random.choice(maps)
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def _score_simulation(self, simulation: AiSimulation, result: dict) -> int:
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game_map = simulation.game.map
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raw_score = self._score_result(game_map, result)
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max_score = self._get_map_max_score(game_map)
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if max_score == 0:
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return 0
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percentage = (raw_score / max_score) * 100.0
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return round(max(0.0, min(100.0, percentage)))
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def _get_map_max_score(self, game_map: Map) -> int:
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result = {}
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result["position"] = game_map.get_first_block_type_position("finish")
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result["coins"] = game_map.count_blocks_of_type("coin")
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result["won"] = True
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result["moves"] = game_map.min_moves
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return self._score_result(game_map, result)
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def _score_result(self, game_map: Map, result: dict) -> int:
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finish = game_map.get_first_block_type_position("finish")
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spawn = game_map.get_first_block_type_position("spawn_point")
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total_coins = game_map.count_blocks_of_type("coin")
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if finish is None or spawn is None:
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distance_progress = 0.0
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else:
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start_distance = abs(spawn[0] - finish[0]) + abs(spawn[1] - finish[1])
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remaining_distance = abs(result["position"][0] - finish[0]) + abs(result["position"][1] - finish[1])
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distance_progress = 1.0 if start_distance == 0 else max(
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0.0, min(1.0, 1.0 - remaining_distance / start_distance)
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)
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steps_progress = 0.0 if result["moves"] == 0 else min(1.0, 1.0 - result["moves"] / game_map.min_moves)
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coin_progress = 0.0 if total_coins == 0 else min(1.0, result["coins"] / total_coins)
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return round(
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self.COINS_WEIGHT * coin_progress
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+ self.DISTANCE_WEIGHT * distance_progress
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+ self.STEPS_WEIGHT * steps_progress
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+ self.WIN_WEIGHT * result["won"]
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)
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@staticmethod
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def _map_payload(game_map: Map) -> dict:
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return {
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"name": game_map.name,
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"difficulty": game_map.difficulty,
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"layout": game_map.to_type_dict(),
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}
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def on_position_update(self) -> None:
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pass
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def on_input_request(self) -> str:
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raise RuntimeError("AI input is supplied one turn at a time in 'step' requests.") |