def undo(self, index):
super(MCTSPlayer, self).undo(index)
self.probability = self.prob_history.pop()
if self.mct.root.parent and self.mct.root.parent.parent:
self.mct.root = self.mct.root.parent.parent
else:
self.mct.root = MCTNode(None, 1.0)
def one_iteration(worker_env):
env = make_env()
evals_before = env.count_evals()
time_before = time.time()
root = construct_root_node(args, env)
mct_root = MCTNode(root)
mct_search(mct_root,
expand_visits=args.mcts_expand,
num_steps=args.mcts_num_steps,
fitness=env.fitness,
finish=env.finish,
is_finished=env.is_finished,
successors=env.successors,
early_end_test=env.early_end_test,
sample_by_urgency=args.mcts_sample_by_urgency,
urgency_method=args.mcts_urgency_method,
urgency_c_uct_explore=args.mcts_urgency_c_uct)
env.cache.print_self("AT END")
if args.print_size_hist:
print_k_histogram(domain)
return mct_root.best_score, env.count_evals(
) - evals_before, time.time() - time_before
def test_mcts(self):
env = domain_parity_apptree.make_env_app_tree()
node = MCTNode(UFTNode(UnfinishedLeaf(), 3))
run_basic_mcts(node, env, 1, 3)
def test_mcts(self):
env = domain_parity_stack.make_env_stack()
node = MCTNode(StackNode([]))
run_basic_mcts(node, env, 1, 3)
def test_mcts(self):
env = domain_koza_stack.make_env_stack(5)
node = MCTNode(StackNode([]))
run_basic_mcts(node, env)
def test_mcts(self):
env = domain_koza_apptree.make_env_app_tree()
node = MCTNode(ChooseKTNode(UnfinishedLeaf(), 5))
run_basic_mcts(node, env)
from mcts import MCTNode, mct_search
from nmcs import nested_mc_search
from domain_koza_apptree import make_env_app_tree
from domain_koza_stack import make_env_stack
from tree_node import ChooseKTNode, StackNode
from tree_stats import TreeStats
from utils import experiment_eval
if __name__ == "__main__":
make_env = make_env_app_tree
#make_env = lambda: make_env_stack(20)
if False:
# MCTS - one run
env = make_env()
# tracer_deco.enable_tracer = True
# random.seed(5)
tree_stats = TreeStats()
# root = MCTNode(ChooseKTNode(UnfinishedLeaf(), 5))
root = MCTNode(StackNode([]))
mct_search(root, expand_visits=8, num_steps=4000,
fitness=env.fitness,
finish=env.finish,
is_finished=env.is_finished,
successors=env.successors,
tree_stats=tree_stats)
print('=' * 20)
print(root.pretty_str())
print(tree_stats.pretty_str())