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human_play.py
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human_play.py
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"""Class containing Human vs AI functions."""
from mcts import MonteCarloTreeSearch, TreeNode
from config import CFG
class HumanPlay(object):
"""Class with functions for a Human vs an AI game.
Attributes:
game: An object containing the game state.
net: An object containing the neural network.
"""
def __init__(self, game, net):
"""Initializes HumanPlay with the board state and neural network."""
self.game = game
self.net = net
def play(self):
"""Function to play a game vs the AI."""
print("Start Human vs AI\n")
mcts = MonteCarloTreeSearch(self.net)
game = self.game.clone() # Create a fresh clone for each game.
game_over = False
value = 0
node = TreeNode()
print("Enter your move in the form: row, column. Eg: 1,1")
go_first = input("Do you want to go first: y/n?")
if go_first.lower().strip() == 'y':
print("You play as X")
human_value = 1
game.print_board()
else:
print("You play as O")
human_value = -1
# 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 as the Human.
# Else play as the AI.
if game.current_player == human_value:
action = input("Enter your move: ")
if isinstance(action, str):
action = [int(n, 10) for n in action.split(",")]
action = (1, action[0], action[1])
best_child = TreeNode()
best_child.action = action
else:
best_child = 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(game.current_player)
best_child.parent = None
node = best_child # Make the child node the root node.
if value == human_value * game.current_player:
print("You won!")
elif value == -human_value * game.current_player:
print("You lost.")
else:
print("Draw Match")
print("\n")