def run_once(debug=True):
# Randomly assigns actors, places, and items for story
root_state = random_state(4, 4)
# Initialize Root Node - Possible Methods boolean MUST BE TRUE
root_node = TreeNode(root_state, parent_edge=None, possible_methods=True)
# Total methods in story
num_methods = len(root_node.possible_methods)
"""
The following
max_numsim = max_expansion * thres
max_iter : Number of sentances in story = number of story nodes - 1 = number of story edges
max_expansion : Number of expansions in search
max_simlength : Maximum length of rollout
C : Exploration Constant for selection
thres : Minimum MCTS Visits for node expansion
"""
# Perform Monte Carlo - returns final node and whole story
max_expansion = 250
if max_expansion < len(root_node.possible_methods):
raise ValueError(
"Max exp ({}) should be greater than num methods({})".format(
max_expansion, len(root_node.possible_methods)))
max_iter = 15
max_simlength = 20
C = 1
thres = 40
minlambda = 0.95
s = Story(root_node)
print(s.create_expository())
#print("Max iteration: {}\nMax Expansion: {}\nMax simulation length: {}\nC: {}\nThreshold: {}".format(max_iter, max_expansion, max_simlength, C, thres))
n, s = mcts(root_node,
max_iter,
max_expansion,
max_simlength,
C,
thres,
mixlambda,
debug=False)
# Print out results
#if debug:
# print(s)
# print(n.believability)
# print(n.value)
# print(percent_goals_satisfied(n, GOALS))
return (n, s)
