evaluate.py

# MIT License
#
# Copyright (c) 2018 Blanyal D'Souza
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
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# The above copyright notice and this permission notice shall be included in all
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# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
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# SOFTWARE.
# ==============================================================================
"""Class to evaluate network."""
from config import CFG
from mcts import TreeNode


class Evaluate(object):
    """Represents the Policy and Value Resnet.

    Attributes:
        current_mcts: An object for the current network's MCTS.
        eval_mcts: An object for the evaluation network's MCTS.
        game: An object containing the game state.
    """

    def __init__(self, current_mcts, eval_mcts, game):
        """Initializes Evaluate with the both network's MCTS and game state."""
        self.current_mcts = current_mcts
        self.eval_mcts = eval_mcts
        self.game = game

    def evaluate(self):
        """Play self-play games between the two networks and record game stats.

        Returns:
            Wins and losses count from the perspective of the current network.
        """
        wins = 0
        losses = 0

        # Self-play loop
        for i in range(CFG.num_eval_games):
            print("Start Evaluation Self-Play Game:", i, "\n")

            game = self.game.clone()  # Create a fresh clone for each game.
            game_over = False
            value = 0
            node = TreeNode()

            player = game.current_player

            # Keep playing until the game is in a terminal state.
            while not game_over:
                # MCTS simulations to get the best child node.
                # If player_to_eval is 1 play using the current network
                # Else play using the evaluation network.
                if game.current_player == 1:
                    best_child = self.current_mcts.search(game, node,
                                                          CFG.temp_final)
                else:
                    best_child = self.eval_mcts.search(game, node,
                                                       CFG.temp_final)

                action = best_child.action
                game.play_action(action)  # Play the child node's action.

                game.print_board()

                game_over, value = game.check_game_over(player)

                best_child.parent = None
                node = best_child  # Make the child node the root node.

            if value == 1:
                print("win")
                wins += 1
            elif value == -1:
                print("loss")
                losses += 1
            else:
                print("draw")
            print("\n")

        return wins, losses