def run():
print(all_scene_numbers)
# create env and agent
env = ThorEnv()
while len(all_scene_numbers) > 0:
lock.acquire()
scene_num = all_scene_numbers.pop()
lock.release()
fn = os.path.join('layouts', ('FloorPlan%d-layout.npy') % scene_num)
if os.path.isfile(fn):
print("file %s already exists; skipping this floorplan" % fn)
continue
openable_json_file = os.path.join(
'layouts', ('FloorPlan%d-openable.json') % scene_num)
scene_objs_json_file = os.path.join(
'layouts', ('FloorPlan%d-objects.json') % scene_num)
scene_name = ('FloorPlan%d') % scene_num
print('Running ' + scene_name)
event = env.reset(scene_name,
render_image=False,
render_depth_image=False,
render_class_image=False,
render_object_image=True)
agent_height = event.metadata['agent']['position']['y']
scene_objs = list(
set([obj['objectType'] for obj in event.metadata['objects']]))
with open(scene_objs_json_file, 'w') as sof:
json.dump(scene_objs, sof, sort_keys=True, indent=4)
# Get all the reachable points through Unity for this step size.
event = env.step(
dict(action='GetReachablePositions',
gridSize=constants.AGENT_STEP_SIZE /
constants.RECORD_SMOOTHING_FACTOR))
if event.metadata['actionReturn'] is None:
print("ERROR: scene %d 'GetReachablePositions' returns None" %
scene_num)
else:
reachable_points = set()
for point in event.metadata['actionReturn']:
reachable_points.add((point['x'], point['z']))
print("scene %d got %d reachable points, now checking" %
(scene_num, len(reachable_points)))
# Pick up a small object to use in testing whether points are good for openable objects.
open_test_objs = {
'ButterKnife', 'CD', 'CellPhone', 'Cloth', 'CreditCard',
'DishSponge', 'Fork', 'KeyChain', 'Pen', 'Pencil', 'SoapBar',
'Spoon', 'Watch'
}
good_obj_point = None
good_obj_point = get_obj(env, open_test_objs, reachable_points,
agent_height, scene_name, good_obj_point)
best_open_point = {
} # map from object names to the best point from which they can be successfully opened
best_sem_coverage = {
} # number of pixels in the semantic map of the receptacle at the existing best openpt
checked_points = set()
scene_receptacles = set()
for point in reachable_points:
point_is_valid = True
action = {
'action': 'TeleportFull',
'x': point[0],
'y': agent_height,
'z': point[1],
}
event = env.step(action)
if event.metadata['lastActionSuccess']:
for horizon in [-30, 0, 30]:
action = {
'action': 'TeleportFull',
'x': point[0],
'y': agent_height,
'z': point[1],
'rotateOnTeleport': True,
'rotation': 0,
'horizon': horizon
}
event = env.step(action)
if not event.metadata['lastActionSuccess']:
point_is_valid = False
break
for rotation in range(3):
action = {'action': 'RotateLeft'}
event = env.step(action)
if not event.metadata['lastActionSuccess']:
point_is_valid = False
break
if not point_is_valid:
break
if point_is_valid:
checked_points.add(point)
else:
continue
# Check whether we can open objects from here in any direction with any tilt.
for rotation in range(4):
# First try up, then down, then return to the horizon before moving again.
for horizon in [-30, 0, 30]:
action = {
'action': 'TeleportFull',
'x': point[0],
'y': agent_height,
'z': point[1],
'rotateOnTeleport': True,
'rotation': rotation * 90,
'horizon': horizon
}
event = env.step(action)
for obj in event.metadata['objects']:
if (obj['visible'] and obj['objectId']
and obj['receptacle']
and not obj['pickupable']
and obj['objectType']
in constants.VAL_RECEPTACLE_OBJECTS):
obj_name = obj['objectId']
obj_point = (obj['position']['x'],
obj['position']['y'])
scene_receptacles.add(obj_name)
# Go ahead and attempt to close the object from this position if it's open.
if obj['openable'] and obj['isOpen']:
close_action = {
'action': 'CloseObject',
'objectId': obj['objectId']
}
event = env.step(close_action)
point_to_recep = np.linalg.norm(
np.array(point) - np.array(obj_point))
if len(env.last_event.
metadata['inventoryObjects']) > 0:
inv_obj = env.last_event.metadata[
'inventoryObjects'][0]['objectId']
else:
inv_obj = None
# Heuristic implemented in task_game_state has agent 0.5 or farther in agent space.
heuristic_far_enough_from_recep = 0.5 < point_to_recep
# Ensure this point affords a larger view according to the semantic segmentation
# of the receptacle than the existing.
point_sem_coverage = get_mask_of_obj(
env, obj['objectId'])
if point_sem_coverage is None:
use_sem_heuristic = False
better_sem_covereage = False
else:
use_sem_heuristic = True
better_sem_covereage = (
obj_name not in best_sem_coverage
or
best_sem_coverage[obj_name] is None
or point_sem_coverage >
best_sem_coverage[obj_name])
# Ensure that this point is farther away than our existing best candidate.
# We'd like to open each receptacle from as far away as possible while retaining
# the ability to pick/place from it.
farther_than_existing_good_point = (
obj_name not in best_open_point
or point_to_recep > np.linalg.norm(
np.array(point) - np.array(
best_open_point[obj_name][:2]))
)
# If we don't have an inventory object, though, we'll fall back to the heuristic
# of being able to open/close as _close_ as possible.
closer_than_existing_good_point = (
obj_name not in best_open_point
or point_to_recep < np.linalg.norm(
np.array(point) - np.array(
best_open_point[obj_name][:2]))
)
# Semantic segmentation heuristic.
if ((use_sem_heuristic
and heuristic_far_enough_from_recep
and better_sem_covereage) or
(not use_sem_heuristic and
# Distance heuristics.
(heuristic_far_enough_from_recep and
(inv_obj and
farther_than_existing_good_point) or
(not inv_obj and
closer_than_existing_good_point)))):
if obj['openable']:
action = {
'action': 'OpenObject',
'objectId': obj['objectId']
}
event = env.step(action)
if not obj[
'openable'] or event.metadata[
'lastActionSuccess']:
# We can open the object, so try placing our small inventory obj inside.
# If it can be placed inside and retrieved, then this is a safe point.
action = {
'action':
'PutObject',
'objectId':
inv_obj,
'receptacleObjectId':
obj['objectId'],
'forceAction':
True,
'placeStationary':
True
}
if inv_obj:
event = env.step(action)
if inv_obj is None or event.metadata[
'lastActionSuccess']:
action = {
'action': 'PickupObject',
'objectId': inv_obj
}
if inv_obj:
event = env.step(action)
if inv_obj is None or event.metadata[
'lastActionSuccess']:
# Finally, ensure we can also close the receptacle.
if obj['openable']:
action = {
'action':
'CloseObject',
'objectId':
obj['objectId']
}
event = env.step(
action)
if not obj['openable'] or event.metadata[
'lastActionSuccess']:
# We can put/pick our inv object into the receptacle from here.
# We have already ensured this point is farther than any
# existing best, so this is the new best.
best_open_point[
obj_name] = [
point[0],
point[1],
rotation * 90,
horizon
]
best_sem_coverage[
obj_name] = point_sem_coverage
# We could not retrieve our inv object, so we need to go get another one
else:
good_obj_point = get_obj(
env, open_test_objs,
reachable_points,
agent_height,
scene_name,
good_obj_point)
action = {
'action':
'TeleportFull',
'x': point[0],
'y': agent_height,
'z': point[1],
'rotateOnTeleport':
True,
'rotation':
rotation * 90,
'horizon': horizon
}
event = env.step(action)
# Regardless of what happened up there, try to close the receptacle again if
# it remained open.
if obj['isOpen']:
action = {
'action': 'CloseObject',
'objectId': obj['objectId']
}
event = env.step(action)
essential_objs = []
if scene_num in constants.SCENE_TYPE["Kitchen"]:
essential_objs.extend(["Microwave", "Fridge"])
for obj in essential_objs:
if not np.any([obj in obj_key for obj_key in best_open_point]):
print(
"WARNING: Essential object %s has no open points in scene %d"
% (obj, scene_num))
print(
"scene %d found open/pick/place/close positions for %d/%d receptacle objects"
% (scene_num, len(best_open_point), len(scene_receptacles)))
with open(openable_json_file, 'w') as f:
json.dump(best_open_point, f, sort_keys=True, indent=4)
print("scene %d reachable %d, checked %d; taking intersection" %
(scene_num, len(reachable_points), len(checked_points)))
points = np.array(list(checked_points))[:, :2]
points = points[np.lexsort((points[:, 0], points[:, 1])), :]
np.save(fn, points)
env.stop()
print('Done')
def main(args, thread_num=0):
print(thread_num)
# settings
alfred_dataset_path = '../data/json_2.1.0/train'
constants.DATA_SAVE_PATH = args.save_path
print("Force Unsave Data: %s" % str(args.force_unsave))
# Set up data structure to track dataset balance and use for selecting next parameters.
# In actively gathering data, we will try to maximize entropy for each (e.g., uniform spread of goals,
# uniform spread over patient objects, uniform recipient objects, and uniform scenes).
succ_traj = pd.DataFrame(
columns=["goal", "pickup", "movable", "receptacle", "scene"])
# objects-to-scene and scene-to-objects database
for scene_type, ids in constants.SCENE_TYPE.items():
for id in ids:
obj_json_file = os.path.join('layouts',
'FloorPlan%d-objects.json' % id)
with open(obj_json_file, 'r') as of:
scene_objs = json.load(of)
id_str = str(id)
scene_id_to_objs[id_str] = scene_objs
for obj in scene_objs:
if obj not in obj_to_scene_ids:
obj_to_scene_ids[obj] = set()
obj_to_scene_ids[obj].add(id_str)
# scene-goal database
for g in constants.GOALS:
for st in constants.GOALS_VALID[g]:
scenes_for_goal[g].extend(
[str(s) for s in constants.SCENE_TYPE[st]])
scenes_for_goal[g] = set(scenes_for_goal[g])
# scene-type database
for st in constants.SCENE_TYPE:
for s in constants.SCENE_TYPE[st]:
scene_to_type[str(s)] = st
# pre-populate counts in this structure using saved trajectories path.
succ_traj, full_traj = load_successes_from_disk(args.save_path, succ_traj,
args.just_examine,
args.repeats_per_cond)
if args.just_examine:
print_successes(succ_traj)
return
print(succ_traj.groupby('goal').count())
# pre-populate failed trajectories.
fail_traj = load_fails_from_disk(args.save_path)
print("Loaded %d known failed tuples" % len(fail_traj))
# create env and agent
env = ThorEnv(x_display='0.%d' % (thread_num % 2))
game_state = TaskGameStateFullKnowledge(env)
agent = DeterministicPlannerAgent(thread_id=0, game_state=game_state)
errors = {
} # map from error strings to counts, to be shown after every failure.
goal_candidates = constants.GOALS[:]
pickup_candidates = list(set().union(*[
constants.VAL_RECEPTACLE_OBJECTS[
obj] # Union objects that can be placed.
for obj in constants.VAL_RECEPTACLE_OBJECTS
]))
pickup_candidates = [
p for p in pickup_candidates
if constants.OBJ_PARENTS[p] in obj_to_scene_ids
]
movable_candidates = list(
set(constants.MOVABLE_RECEPTACLES).intersection(
obj_to_scene_ids.keys()))
receptacle_candidates = [obj for obj in constants.VAL_RECEPTACLE_OBJECTS
if obj not in constants.MOVABLE_RECEPTACLES and obj in obj_to_scene_ids] + \
[obj for obj in constants.VAL_ACTION_OBJECTS["Toggleable"]
if obj in obj_to_scene_ids]
# toaster isn't interesting in terms of producing linguistic diversity
receptacle_candidates.remove('Toaster')
receptacle_candidates.sort()
scene_candidates = list(scene_id_to_objs.keys())
n_until_load_successes = args.async_load_every_n_samples
print_successes(succ_traj)
task_sampler = sample_task_params(succ_traj, full_traj, fail_traj,
goal_candidates, pickup_candidates,
movable_candidates,
receptacle_candidates, scene_candidates)
# main generation loop
# keeps trying out new task tuples as trajectories either fail or suceed
while True:
# for _ in range(20):
for ii, json_path in enumerate(
glob.iglob(os.path.join(alfred_dataset_path, "**",
"traj_data.json"),
recursive=True)):
# if ii % args.num_threads == thread_num:
# if ii == 5:
sampled_task = json_path.split('/')[-3].split('-')
# sampled_task = next(task_sampler)
# print("===============")
# print(ii, json_path)
print(sampled_task) # DEBUG
# print("===============")
if sampled_task is None:
sys.exit(
"No valid tuples left to sample (all are known to fail or already have %d trajectories"
% args.repeats_per_cond)
gtype, pickup_obj, movable_obj, receptacle_obj, sampled_scene = sampled_task
sampled_scene = int(sampled_scene)
print("sampled tuple: " + str((gtype, pickup_obj, movable_obj,
receptacle_obj, sampled_scene)))
tries_remaining = args.trials_before_fail
# only try to get the number of trajectories left to make this tuple full.
target_remaining = args.repeats_per_cond - len(
succ_traj.loc[(succ_traj['goal'] == gtype)
& (succ_traj['pickup'] == pickup_obj) &
(succ_traj['movable'] == movable_obj) &
(succ_traj['receptacle'] == receptacle_obj) &
(succ_traj['scene'] == str(sampled_scene))])
num_place_fails = 0 # count of errors related to placement failure for no valid positions.
# continue until we're (out of tries + have never succeeded) or (have gathered the target number of instances)
while num_place_fails > args.trials_before_fail or target_remaining > 0:
# environment setup
constants.pddl_goal_type = gtype
print("PDDLGoalType: " + constants.pddl_goal_type)
task_id = create_dirs(gtype, pickup_obj, movable_obj,
receptacle_obj, sampled_scene)
# setup data dictionary
setup_data_dict()
constants.data_dict['task_id'] = task_id
constants.data_dict['task_type'] = constants.pddl_goal_type
constants.data_dict['dataset_params'][
'video_frame_rate'] = constants.VIDEO_FRAME_RATE
# plan & execute
try:
# if True:
# Agent reset to new scene.
constraint_objs = {
'repeat': [(
constants.OBJ_PARENTS[
pickup_obj], # Generate multiple parent objs.
np.random.randint(
2 if gtype == "pick_two_obj_and_place" else 1,
constants.PICKUP_REPEAT_MAX + 1))],
'sparse':
[(receptacle_obj.replace('Basin', ''),
num_place_fails * constants.RECEPTACLE_SPARSE_POINTS)
]
}
if movable_obj != "None":
constraint_objs['repeat'].append(
(movable_obj,
np.random.randint(1, constants.PICKUP_REPEAT_MAX +
1)))
for obj_type in scene_id_to_objs[str(sampled_scene)]:
if (obj_type in pickup_candidates and
obj_type != constants.OBJ_PARENTS[pickup_obj]
and obj_type != movable_obj):
constraint_objs['repeat'].append(
(obj_type,
np.random.randint(
1,
constants.MAX_NUM_OF_OBJ_INSTANCES + 1)))
if gtype in goal_to_invalid_receptacle:
constraint_objs['empty'] = [
(r.replace('Basin', ''), num_place_fails *
constants.RECEPTACLE_EMPTY_POINTS)
for r in goal_to_invalid_receptacle[gtype]
]
constraint_objs['seton'] = []
if gtype == 'look_at_obj_in_light':
constraint_objs['seton'].append(
(receptacle_obj, False))
if num_place_fails > 0:
print(
"Failed %d placements in the past; increased free point constraints: "
% num_place_fails + str(constraint_objs))
scene_info = {
'scene_num': sampled_scene,
'random_seed': random.randint(0, 2**32)
}
info = agent.reset(scene=scene_info, objs=constraint_objs)
# Problem initialization with given constraints.
task_objs = {'pickup': pickup_obj}
if movable_obj != "None":
task_objs['mrecep'] = movable_obj
if gtype == "look_at_obj_in_light":
task_objs['toggle'] = receptacle_obj
else:
task_objs['receptacle'] = receptacle_obj
agent.setup_problem({'info': info},
scene=scene_info,
objs=task_objs)
# Now that objects are in their initial places, record them.
object_poses = [{
'objectName':
obj['name'].split('(Clone)')[0],
'position':
obj['position'],
'rotation':
obj['rotation']
} for obj in env.last_event.metadata['objects']
if obj['pickupable']]
dirty_and_empty = gtype == 'pick_clean_then_place_in_recep'
object_toggles = [{
'objectType': o,
'stateChange': 'toggleable',
'isToggled': v
} for o, v in constraint_objs['seton']]
constants.data_dict['scene']['object_poses'] = object_poses
constants.data_dict['scene'][
'dirty_and_empty'] = dirty_and_empty
constants.data_dict['scene'][
'object_toggles'] = object_toggles
# Pre-restore the scene to cause objects to "jitter" like they will when the episode is replayed
# based on stored object and toggle info. This should put objects closer to the final positions they'll
# be inlay at inference time (e.g., mugs fallen and broken, knives fallen over, etc.).
print("Performing reset via thor_env API")
env.reset(sampled_scene)
print("Performing restore via thor_env API")
env.restore_scene(object_poses, object_toggles,
dirty_and_empty)
event = env.step(
dict(constants.data_dict['scene']['init_action']))
terminal = False
while not terminal and agent.current_frame_count <= constants.MAX_EPISODE_LENGTH:
action_dict = agent.get_action(None)
agent.step(action_dict)
reward, terminal = agent.get_reward()
dump_data_dict()
save_video()
# else:
except Exception as e:
import traceback
traceback.print_exc()
print("Error: " + repr(e))
print("Invalid Task: skipping...")
if args.debug:
print(traceback.format_exc())
deleted = delete_save(args.in_parallel)
if not deleted: # another thread is filling this task successfully, so leave it alone.
target_remaining = 0 # stop trying to do this task.
else:
if str(
e
) == "API Action Failed: No valid positions to place object found":
# Try increasing the space available on sparse and empty flagged objects.
num_place_fails += 1
tries_remaining -= 1
else: # generic error
tries_remaining -= 1
estr = str(e)
if len(estr) > 120:
estr = estr[:120]
if estr not in errors:
errors[estr] = 0
errors[estr] += 1
print("%%%%%%%%%%")
es = sum([errors[er] for er in errors])
print("\terrors (%d):" % es)
for er, v in sorted(errors.items(),
key=lambda kv: kv[1],
reverse=True):
if v / es < 0.01: # stop showing below 1% of errors.
break
print("\t(%.2f) (%d)\t%s" % (v / es, v, er))
print("%%%%%%%%%%")
continue
if args.force_unsave:
delete_save(args.in_parallel)
# add to save structure.
succ_traj = succ_traj.append(
{
"goal": gtype,
"movable": movable_obj,
"pickup": pickup_obj,
"receptacle": receptacle_obj,
"scene": str(sampled_scene)
},
ignore_index=True)
target_remaining -= 1
tries_remaining += args.trials_before_fail # on success, add more tries for future successes
# if this combination resulted in a certain number of failures with no successes, flag it as not possible.
if tries_remaining == 0 and target_remaining == args.repeats_per_cond:
new_fails = [(gtype, pickup_obj, movable_obj, receptacle_obj,
str(sampled_scene))]
fail_traj = load_fails_from_disk(args.save_path,
to_write=new_fails)
print("%%%%%%%%%%")
print("failures (%d)" % len(fail_traj))
# print("\t" + "\n\t".join([str(ft) for ft in fail_traj]))
print("%%%%%%%%%%")
# if this combination gave us the repeats we wanted, note it as filled.
if target_remaining == 0:
full_traj.add((gtype, pickup_obj, movable_obj, receptacle_obj,
sampled_scene))
# if we're sharing with other processes, reload successes from disk to update local copy with others' additions.
if args.in_parallel:
if n_until_load_successes > 0:
n_until_load_successes -= 1
else:
print(
"Reloading trajectories from disk because of parallel processes..."
)
succ_traj = pd.DataFrame(
columns=succ_traj.columns) # Drop all rows.
succ_traj, full_traj = load_successes_from_disk(
args.save_path, succ_traj, False,
args.repeats_per_cond)
print("... Loaded %d trajectories" % len(succ_traj.index))
n_until_load_successes = args.async_load_every_n_samples
print_successes(succ_traj)
task_sampler = sample_task_params(
succ_traj, full_traj, fail_traj, goal_candidates,
pickup_candidates, movable_candidates,
receptacle_candidates, scene_candidates)
print(
"... Created fresh instance of sample_task_params generator"
)
class Thor(threading.Thread):
def __init__(self, queue, train_eval="train"):
Thread.__init__(self)
self.action_queue = queue
self.mask_rcnn = None
self.env = None
self.train_eval = train_eval
self.controller_type = "oracle"
def run(self):
while True:
action, reset, task_file = self.action_queue.get()
try:
if reset:
self.reset(task_file)
else:
self.step(action)
finally:
self.action_queue.task_done()
def init_env(self, config):
self.config = config
screen_height = config['env']['thor']['screen_height']
screen_width = config['env']['thor']['screen_width']
smooth_nav = config['env']['thor']['smooth_nav']
save_frames_to_disk = config['env']['thor']['save_frames_to_disk']
if not self.env:
self.env = ThorEnv(player_screen_height=screen_height,
player_screen_width=screen_width,
smooth_nav=smooth_nav,
save_frames_to_disk=save_frames_to_disk)
self.controller_type = self.config['controller']['type']
self._done = False
self._res = ()
self._feedback = ""
self.expert = HandCodedThorAgent(self.env, max_steps=200)
self.prev_command = ""
self.load_mask_rcnn()
def load_mask_rcnn(self):
# load pretrained MaskRCNN model if required
if 'mrcnn' in self.config['controller'][
'type'] and not self.mask_rcnn:
model_path = os.path.join(
os.environ['ALFRED_ROOT'],
self.config['mask_rcnn']['pretrained_model_path'])
self.mask_rcnn = load_pretrained_model(model_path)
def set_task(self, task_file):
self.task_file = task_file
self.traj_root = os.path.dirname(task_file)
with open(task_file, 'r') as f:
self.traj_data = json.load(f)
def reset(self, task_file):
assert self.env
assert self.controller_type
self.set_task(task_file)
# scene setup
scene_num = self.traj_data['scene']['scene_num']
object_poses = self.traj_data['scene']['object_poses']
dirty_and_empty = self.traj_data['scene']['dirty_and_empty']
object_toggles = self.traj_data['scene']['object_toggles']
scene_name = 'FloorPlan%d' % scene_num
self.env.reset(scene_name)
self.env.restore_scene(object_poses, object_toggles,
dirty_and_empty)
# recording
save_frames_path = self.config['env']['thor']['save_frames_path']
self.env.save_frames_path = os.path.join(
save_frames_path, self.traj_root.replace('../', ''))
# initialize to start position
self.env.step(dict(
self.traj_data['scene']['init_action'])) # print goal instr
task_desc = get_templated_task_desc(self.traj_data)
print("Task: %s" % task_desc)
# print("Task: %s" % (self.traj_data['turk_annotations']['anns'][0]['task_desc']))
# setup task for reward
class args:
pass
args.reward_config = os.path.join(os.environ['ALFRED_ROOT'],
'agents/config/rewards.json')
self.env.set_task(self.traj_data, args, reward_type='dense')
# set controller
self.controller_type = self.config['controller']['type']
self.goal_desc_human_anns_prob = self.config['env'][
'goal_desc_human_anns_prob']
load_receps = self.config['controller']['load_receps']
debug = self.config['controller']['debug']
if self.controller_type == 'oracle':
self.controller = OracleAgent(
self.env,
self.traj_data,
self.traj_root,
load_receps=load_receps,
debug=debug,
goal_desc_human_anns_prob=self.goal_desc_human_anns_prob)
elif self.controller_type == 'oracle_astar':
self.controller = OracleAStarAgent(
self.env,
self.traj_data,
self.traj_root,
load_receps=load_receps,
debug=debug,
goal_desc_human_anns_prob=self.goal_desc_human_anns_prob)
elif self.controller_type == 'mrcnn':
self.controller = MaskRCNNAgent(
self.env,
self.traj_data,
self.traj_root,
pretrained_model=self.mask_rcnn,
load_receps=load_receps,
debug=debug,
goal_desc_human_anns_prob=self.goal_desc_human_anns_prob,
save_detections_to_disk=self.env.save_frames_to_disk,
save_detections_path=self.env.save_frames_path)
elif self.controller_type == 'mrcnn_astar':
self.controller = MaskRCNNAStarAgent(
self.env,
self.traj_data,
self.traj_root,
pretrained_model=self.mask_rcnn,
load_receps=load_receps,
debug=debug,
goal_desc_human_anns_prob=self.goal_desc_human_anns_prob,
save_detections_to_disk=self.env.save_frames_to_disk,
save_detections_path=self.env.save_frames_path)
else:
raise NotImplementedError()
# zero steps
self.steps = 0
# reset expert state
self.expert.reset(task_file)
self.prev_command = ""
# return intro text
self._feedback = self.controller.feedback
self._res = self.get_info()
return self._feedback
def step(self, action):
if not self._done:
# take action
self.prev_command = str(action)
self._feedback = self.controller.step(action)
self._res = self.get_info()
if self.env.save_frames_to_disk:
self.record_action(action)
self.steps += 1
def get_results(self):
return self._res
def record_action(self, action):
txt_file = os.path.join(self.env.save_frames_path, 'action.txt')
with open(txt_file, 'a+') as f:
f.write("%s\r\n" % str(action))
def get_info(self):
won = self.env.get_goal_satisfied()
pcs = self.env.get_goal_conditions_met()
goal_condition_success_rate = pcs[0] / float(pcs[1])
acs = self.controller.get_admissible_commands()
# expert action
if self.train_eval == "train":
game_state = {
'admissible_commands': acs,
'feedback': self._feedback,
'won': won
}
expert_actions = ["look"]
try:
if not self.prev_command:
self.expert.observe(game_state['feedback'])
else:
next_action = self.expert.act(game_state, 0, won,
self.prev_command)
if next_action in acs:
expert_actions = [next_action]
except HandCodedAgentTimeout:
print("Expert Timeout")
except Exception as e:
print(e)
traceback.print_exc()
else:
expert_actions = []
training_method = self.config["general"]["training_method"]
if training_method == "dqn":
max_nb_steps_per_episode = self.config["rl"]["training"][
"max_nb_steps_per_episode"]
elif training_method == "dagger":
max_nb_steps_per_episode = self.config["dagger"]["training"][
"max_nb_steps_per_episode"]
else:
raise NotImplementedError
self._done = won or self.steps > max_nb_steps_per_episode
return (self._feedback, self._done, acs, won,
goal_condition_success_rate, expert_actions)
def get_last_frame(self):
return self.env.last_event.frame[:, :, ::-1]
def get_exploration_frames(self):
return self.controller.get_exploration_frames()