def plan(timeout, blocks, problem, model):
tree = Tree(blocks)
for t in range(timeout):
parent_node_id = tree.get_exp_best_node_expand()
#print(t, len(tree.nodes[parent_node_id]['tower']), tree.nodes[parent_node_id]['value'])
sys.stdout.write("Search progress: %i \r" % (t))
sys.stdout.flush()
new_nodes = problem.sample_actions(tree.nodes[parent_node_id], model)
for node in new_nodes:
tree.expand(parent_node_id, node)
return tree
def plan_mcts(logger, timeout, blocks, problem, model, c=1., discrete=True):
tree = Tree(blocks)
tallest_tower = [0]
highest_exp_height = [0]
highest_value = [0]
tower_stats = np.zeros((problem.max_height, timeout))
node_values = {
k: {
'median': [],
'25': [],
'75': []
}
for k in range(problem.max_height + 1)
}
for t in range(timeout):
tower_stats[:, t] = tower_stats[:, t - 1]
sys.stdout.write("Search progress: %i \r" % (t))
sys.stdout.flush()
parent_node_id = tree.traverse(c)
new_nodes = problem.sample_actions(tree.nodes[parent_node_id],
model,
discrete=discrete)
tallest_tower_t = tallest_tower[-1]
highest_exp_height_t = highest_exp_height[-1]
highest_value_t = highest_value[-1]
for new_node in new_nodes:
#print(t, len(new_node['tower']), new_node['exp_reward'])
new_node_id = tree.expand(parent_node_id, new_node)
rollout_value = tree.rollout(new_node_id, problem, model)
tree.backpropagate(new_node_id, rollout_value)
tower_height = len(new_node['tower'])
#print(tower_height)
index = int(tower_height)
tower_stats[index - 1, t] += 1
if len(new_node['tower']) > tallest_tower_t:
tallest_tower_t = len(new_node['tower'])
if new_node['exp_reward'] > highest_exp_height_t:
highest_exp_height_t = new_node['exp_reward']
if new_node['value'] > highest_value_t:
highest_value_t = new_node['value']
tallest_tower.append(tallest_tower_t)
highest_exp_height.append(highest_exp_height_t)
highest_value.append(highest_value_t)
# update node value stats
temp_values = {k: [] for k in range(problem.max_height + 1)}
for node in tree.nodes:
height = len(tree.nodes[node]['tower'])
temp_values[height].append(tree.nodes[node]['value'])
for height in range(problem.max_height + 1):
if temp_values[height] == []:
node_values[height]['median'].append(0)
node_values[height]['25'].append(0)
node_values[height]['75'].append(0)
else:
node_values[height]['median'].append(
np.median(temp_values[height]))
node_values[height]['25'].append(
np.quantile(temp_values[height], .25))
node_values[height]['75'].append(
np.quantile(temp_values[height], .75))
return tree, tallest_tower, highest_exp_height, highest_value, tower_stats, node_values