def think(state):
""" Performs MCTS by sampling games and calling the appropriate functions to construct the game tree.
Args:
state: The state of the game.
Returns: The action to be taken.
"""
#'''
start = time()
time_elapsed = 0
#'''
def get_result (sampled_game):
reds, blues = sampled_game.score.get('red', 0), sampled_game.score.get('blue', 0)
result = reds - blues if identity_of_bot == 'red' else blues - reds
return result
identity_of_bot = state.player_turn
root_node = MCTSNode(parent=None, parent_action=None, action_list=state.legal_moves)
while time_elapsed < 10:
#for step in range(num_nodes):
# Copy the game for sampling a playthrough
sampled_game = state.copy()
# Start at root
node = root_node
# Do MCTS - This is all you!
node = traverse_nodes(node, sampled_game, identity_of_bot)
node = expand_leaf(node, sampled_game)
rollout(sampled_game)
backpropagate(node, get_result(sampled_game))
time_elapsed = time() - start
# Make choice based on tree
choice = make_choice(root_node, state, identity_of_bot)
action = choice.parent_action
# Write tree to file for time testing (Extra Credit Assignment)
file = open('mcts_modified.out', 'a')
file.write(root_node.tree_to_string(horizon=100, indent=1))
return action
# Return an action, typically the most frequently used action (from the root) or the action with the best
# estimated win rate.
def think(state):
""" Performs MCTS by sampling games and calling the appropriate functions to construct the game tree.
Args:
state: The state of the game.
Returns: The action to be taken.
"""
start = time()
time_elapsed = 0
def get_result (sampled_game):
if sampled_game.winner == identity_of_bot: return 1
if sampled_game.winner == 'tie': return 0.5
if sampled_game.winner != identity_of_bot: return -1
else: return -1
identity_of_bot = state.player_turn
root_node = MCTSNode(parent=None, parent_action=None, action_list=state.legal_moves)
while time_elapsed < 10:
#for step in range(num_nodes):
# Copy the game for sampling a playthrough
sampled_game = state.copy()
# Start at root
node = root_node
# Do MCTS - This is all you!
node = traverse_nodes(node, sampled_game, identity_of_bot)
node = expand_leaf(node, sampled_game)
rollout(sampled_game)
backpropagate(node, get_result(sampled_game))
time_elapsed = time() - start
# Make choice based on tree
choice = make_choice(root_node, state, identity_of_bot)
action = choice.parent_action
# Write tree to file for time testing (Extra Credit Assignment)
file = open('mcts_vanilla.out', 'a')
file.write(root_node.tree_to_string(horizon=100, indent=1))
return action
