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dmlab30.py
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dmlab30.py
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# Copyright 2018 Google LLC
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Utilities for DMLab-30."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import collections
import numpy as np
import tensorflow as tf
LEVEL_MAPPING = collections.OrderedDict([
('rooms_collect_good_objects_train', 'rooms_collect_good_objects_test'),
('rooms_exploit_deferred_effects_train',
'rooms_exploit_deferred_effects_test'),
('rooms_select_nonmatching_object', 'rooms_select_nonmatching_object'),
('rooms_watermaze', 'rooms_watermaze'),
('rooms_keys_doors_puzzle', 'rooms_keys_doors_puzzle'),
('language_select_described_object', 'language_select_described_object'),
('language_select_located_object', 'language_select_located_object'),
('language_execute_random_task', 'language_execute_random_task'),
('language_answer_quantitative_question',
'language_answer_quantitative_question'),
('lasertag_one_opponent_small', 'lasertag_one_opponent_small'),
('lasertag_three_opponents_small', 'lasertag_three_opponents_small'),
('lasertag_one_opponent_large', 'lasertag_one_opponent_large'),
('lasertag_three_opponents_large', 'lasertag_three_opponents_large'),
('natlab_fixed_large_map', 'natlab_fixed_large_map'),
('natlab_varying_map_regrowth', 'natlab_varying_map_regrowth'),
('natlab_varying_map_randomized', 'natlab_varying_map_randomized'),
('skymaze_irreversible_path_hard', 'skymaze_irreversible_path_hard'),
('skymaze_irreversible_path_varied', 'skymaze_irreversible_path_varied'),
('psychlab_arbitrary_visuomotor_mapping',
'psychlab_arbitrary_visuomotor_mapping'),
('psychlab_continuous_recognition', 'psychlab_continuous_recognition'),
('psychlab_sequential_comparison', 'psychlab_sequential_comparison'),
('psychlab_visual_search', 'psychlab_visual_search'),
('explore_object_locations_small', 'explore_object_locations_small'),
('explore_object_locations_large', 'explore_object_locations_large'),
('explore_obstructed_goals_small', 'explore_obstructed_goals_small'),
('explore_obstructed_goals_large', 'explore_obstructed_goals_large'),
('explore_goal_locations_small', 'explore_goal_locations_small'),
('explore_goal_locations_large', 'explore_goal_locations_large'),
('explore_object_rewards_few', 'explore_object_rewards_few'),
('explore_object_rewards_many', 'explore_object_rewards_many'),
])
HUMAN_SCORES = {
'rooms_collect_good_objects_test': 10,
'rooms_exploit_deferred_effects_test': 85.65,
'rooms_select_nonmatching_object': 65.9,
'rooms_watermaze': 54,
'rooms_keys_doors_puzzle': 53.8,
'language_select_described_object': 389.5,
'language_select_located_object': 280.7,
'language_execute_random_task': 254.05,
'language_answer_quantitative_question': 184.5,
'lasertag_one_opponent_small': 12.65,
'lasertag_three_opponents_small': 18.55,
'lasertag_one_opponent_large': 18.6,
'lasertag_three_opponents_large': 31.5,
'natlab_fixed_large_map': 36.9,
'natlab_varying_map_regrowth': 24.45,
'natlab_varying_map_randomized': 42.35,
'skymaze_irreversible_path_hard': 100,
'skymaze_irreversible_path_varied': 100,
'psychlab_arbitrary_visuomotor_mapping': 58.75,
'psychlab_continuous_recognition': 58.3,
'psychlab_sequential_comparison': 39.5,
'psychlab_visual_search': 78.5,
'explore_object_locations_small': 74.45,
'explore_object_locations_large': 65.65,
'explore_obstructed_goals_small': 206,
'explore_obstructed_goals_large': 119.5,
'explore_goal_locations_small': 267.5,
'explore_goal_locations_large': 194.5,
'explore_object_rewards_few': 77.7,
'explore_object_rewards_many': 106.7,
}
RANDOM_SCORES = {
'rooms_collect_good_objects_test': 0.073,
'rooms_exploit_deferred_effects_test': 8.501,
'rooms_select_nonmatching_object': 0.312,
'rooms_watermaze': 4.065,
'rooms_keys_doors_puzzle': 4.135,
'language_select_described_object': -0.07,
'language_select_located_object': 1.929,
'language_execute_random_task': -5.913,
'language_answer_quantitative_question': -0.33,
'lasertag_one_opponent_small': -0.224,
'lasertag_three_opponents_small': -0.214,
'lasertag_one_opponent_large': -0.083,
'lasertag_three_opponents_large': -0.102,
'natlab_fixed_large_map': 2.173,
'natlab_varying_map_regrowth': 2.989,
'natlab_varying_map_randomized': 7.346,
'skymaze_irreversible_path_hard': 0.1,
'skymaze_irreversible_path_varied': 14.4,
'psychlab_arbitrary_visuomotor_mapping': 0.163,
'psychlab_continuous_recognition': 0.224,
'psychlab_sequential_comparison': 0.129,
'psychlab_visual_search': 0.085,
'explore_object_locations_small': 3.575,
'explore_object_locations_large': 4.673,
'explore_obstructed_goals_small': 6.76,
'explore_obstructed_goals_large': 2.61,
'explore_goal_locations_small': 7.66,
'explore_goal_locations_large': 3.14,
'explore_object_rewards_few': 2.073,
'explore_object_rewards_many': 2.438,
}
ALL_LEVELS = frozenset([
'rooms_collect_good_objects_train',
'rooms_collect_good_objects_test',
'rooms_exploit_deferred_effects_train',
'rooms_exploit_deferred_effects_test',
'rooms_select_nonmatching_object',
'rooms_watermaze',
'rooms_keys_doors_puzzle',
'language_select_described_object',
'language_select_located_object',
'language_execute_random_task',
'language_answer_quantitative_question',
'lasertag_one_opponent_small',
'lasertag_three_opponents_small',
'lasertag_one_opponent_large',
'lasertag_three_opponents_large',
'natlab_fixed_large_map',
'natlab_varying_map_regrowth',
'natlab_varying_map_randomized',
'skymaze_irreversible_path_hard',
'skymaze_irreversible_path_varied',
'psychlab_arbitrary_visuomotor_mapping',
'psychlab_continuous_recognition',
'psychlab_sequential_comparison',
'psychlab_visual_search',
'explore_object_locations_small',
'explore_object_locations_large',
'explore_obstructed_goals_small',
'explore_obstructed_goals_large',
'explore_goal_locations_small',
'explore_goal_locations_large',
'explore_object_rewards_few',
'explore_object_rewards_many',
])
def _transform_level_returns(level_returns):
"""Converts training level names to test level names."""
new_level_returns = {}
for level_name, returns in level_returns.iteritems():
new_level_returns[LEVEL_MAPPING.get(level_name, level_name)] = returns
test_set = set(LEVEL_MAPPING.values())
diff = test_set - set(new_level_returns.keys())
if diff:
raise ValueError('Missing levels: %s' % list(diff))
for level_name, returns in new_level_returns.iteritems():
if level_name in test_set:
if not returns:
raise ValueError('Missing returns for level: \'%s\': ' % level_name)
else:
tf.logging.info('Skipping level %s for calculation.', level_name)
return new_level_returns
def compute_human_normalized_score(level_returns, per_level_cap):
"""Computes human normalized score.
Levels that have different training and test versions, will use the returns
for the training level to calculate the score. E.g.
'rooms_collect_good_objects_train' will be used for
'rooms_collect_good_objects_test'. All returns for levels not in DmLab-30
will be ignored.
Args:
level_returns: A dictionary from level to list of episode returns.
per_level_cap: A percentage cap (e.g. 100.) on the per level human
normalized score. If None, no cap is applied.
Returns:
A float with the human normalized score in percentage.
Raises:
ValueError: If a level is missing from `level_returns` or has no returns.
"""
new_level_returns = _transform_level_returns(level_returns)
def human_normalized_score(level_name, returns):
score = np.mean(returns)
human = HUMAN_SCORES[level_name]
random = RANDOM_SCORES[level_name]
human_normalized_score = (score - random) / (human - random) * 100
if per_level_cap is not None:
human_normalized_score = min(human_normalized_score, per_level_cap)
return human_normalized_score
return np.mean(
[human_normalized_score(k, v) for k, v in new_level_returns.items()])