def get_trajectories(self, full_states):
# print(self.head)
obj_dumps = [s[1] for s in full_states]
trajectory = get_individual_data(self.head, obj_dumps, pos_val_hash=1)
# TODO: automatically determine if correlate pos_val_hash is 1 or 2
# TODO: multiple tail support
# TODO: Separation of Interference and Contingent objects
if self.tail[0] == "Action":
# print(obj_dumps, self.tail[0])
merged = trajectory
# correlate_trajectory = get_individual_data(self.tail[0], obj_dumps, pos_val_hash=2)
else:
correlate_trajectory = get_individual_data(self.tail[0], obj_dumps, pos_val_hash=1)
merged = np.concatenate([trajectory, correlate_trajectory], axis=1)
# print(pytorch_model.wrap(merged))
return pytorch_model.wrap(merged).cuda()
def get_state(self, state): # copy of get_trajectories, but for a single state
# print(self.head)
if self.head == "Block": # TODO: make not hard coded
hstate = get_individual_data(self.head, [state[1]], pos_val_hash=3)[0]
else:
hstate = get_individual_data(self.head, [state[1]], pos_val_hash=1)[0]
# TODO: automatically determine if correlate pos_val_hash is 1 or 2
# TODO: multiple tail support
# TODO: Separation of Interference and Contingent objects
if self.tail[0] == "Action":
# print(obj_dumps, self.tail[0])
merged = hstate
corr_state = []
# correlate_trajectory = get_individual_data(self.tail[0], obj_dumps, pos_val_hash=2)
else:
corr_state = get_individual_data(self.tail[0], [state[1]], pos_val_hash=1)[0]
merged = np.concatenate([hstate, corr_state])
# print(pytorch_model.wrap(merged))
return merged, [len(hstate), len(corr_state)]
# python get_reward.py --record-rollouts data/ataripaddle/ --changepoint-dir data/atarigraph/ --train-edge "Paddle->Ball" --transforms WProx --determiner prox --reward-form changepoint --num-stack 1 --focus-dumps-name focus_dumps.txt --dp-gmm atariball --period 5
# python get_reward.py --record-rollouts data/pusherrandom/ --changepoint-dir data/fullpusher/ --train-edge "Action->Gripper" --transforms SVel SCorAvg --determiner overlap --reward-form markov --segment --train --num-stack 2 --gpu 1
# python get_reward.py --record-rollouts data/extragripper/ --changepoint-dir data/pushergraphvec/ --train-edge "Gripper->Block" --transforms SProxVel --determiner merged --reward-form changepoint --segment --num-stack 2 --gpu 1 --cluster-model FDPGMM --period 9 --dp-gmm block --min-cluster 5
# python get_reward.py --record-rollouts data/pusherrandom/ --changepoint-dir data/fullpusher/ --train-edge "Action->Gripper" --transforms SVel SCorAvg --determiner overlap --reward-form markov --segment --train --num-stack 2 --gpu 1 > pusher/reward_training.txt
dataset_path = args.record_rollouts
changepoints_path = args.record_rollouts # these are the same for creating rewards
head, tail = get_edge(args.train_edge)
cp_dict = load_from_pickle(
os.path.join(changepoints_path, "changepoints-" + head + ".pkl"))
changepoints, models = get_cp_models_from_dict(cp_dict)
obj_dumps = read_obj_dumps(dataset_path,
i=-1,
rng=args.num_iters,
filename=args.focus_dumps_name)
trajectory = get_individual_data(head, obj_dumps, pos_val_hash=1)
# TODO: automatically determine if correlate pos_val_hash is 1 or 2
# TODO: multiple tail support
if tail[0] == "Action":
correlate_trajectory = get_individual_data(tail[0],
obj_dumps,
pos_val_hash=2)
new_ct = np.zeros(
(len(correlate_trajectory), int(np.max(correlate_trajectory)) + 1))
hot_idxes = np.array(list(range(
len(correlate_trajectory)))), correlate_trajectory.astype(int)
new_ct[np.array(list(range(len(correlate_trajectory)))),
np.squeeze(correlate_trajectory.astype(int))] = 1
correlate_trajectory = new_ct
else:
correlate_trajectory = get_individual_data(tail[0],